Characterizing the lavage and serum cytokine profiles of interstitial pneumonia with autoimmune features and their implications for progressive fibrosis.

OBJECTIVE: To explore whether cytokines could be potential biomarkers to predict the occurrence of progressive fibrosis (PF) phenotype among interstitial pneumonia with autoimmune features (IPAF) patients. METHODS: This study prospectively collected 51 IPAF and 15 Idiopathic Pulmonary Fibrosis (IPF) patients who were diagnosed at First Affiliated Hospital of Guangzhou Medical University from July 2020 to June 2021. All IPAF patients were followed up for one year to assess the development of PF phenotype. Paired Broncho Alveolar Lavage Fluid (BALF) and serum samples were collected at enrolment and analyzed for differences in 39 cytokines expression. Principal component analysis (PCA) and cluster analysis were conducted to identify a high-risk subgroup of IPAF patients for developing the PF phenotype. Finally, cytokine differences were compared between subgroups to identify potential biomarkers for PF-IPAF occurrence. RESULTS: According to the PCA analysis, 81.25% of PF-IPAF patients share overlapped BALF cytokine profiles with IPF. Cluster analysis indicated IPAF patients in subtype 2 had a higher risk to develop PF phenotype within one year (P = 0.048), characterized by higher levels of CCL2, CXCL12 and lower lymphocyte proportion (LYM%) in BALF. Elevated levels of BALF CCL2 (>299.16 pg./ml) or CXCL12 (>600.115 pg./ml) were associated with a significantly higher risk of developing PF phenotype within one year follow-up period (P = 0.009, 0.001). CONCLUSION: PF-IPAF phenotype exhibits similar inflammatory cytokine profiles to IPF. Cytokine CCL2, CXCL12, and LYM% in BALF serving as potential biomarkers for predicting the PF phenotype in IPAF patients. CLINICAL TRIAL REGISTRATION: Register: Qian Han, Website: http://www.chictr.org.cn/showproj.aspx?proj=61619, Registration number: ChiCTR2000040998.

Complex Involvement of the Extracellular Matrix, Immune Effect, and Lipid Metabolism in the Development of Idiopathic Pulmonary Fibrosis.

Background and objective: Idiopathic pulmonary fibrosis (IPF) is an aggressive fibrotic pulmonary disease with spatially and temporally heterogeneous alveolar lesions. There are no early diagnostic biomarkers, limiting our understanding of IPF pathogenesis. Methods: Lung tissue from surgical lung biopsy of patients with early-stage IPF (n = 7), transplant-stage IPF (n = 2), and healthy controls (n = 6) were subjected to mRNA sequencing and verified by real-time quantitative PCR (RT-qPCR), immunohistochemistry, Western blot, and single-cell RNA sequencing (scRNA-Seq). Results: Three hundred eighty differentially expressed transcripts (DETs) were identified in IPF that were principally involved in extracellular matrix (ECM) remodeling, lipid metabolism, and immune effect. Of these DETs, 21 (DMD, MMP7, POSTN, ECM2, MMP13, FASN, FADS1, SDR16C5, ACAT2, ACSL1, CYP1A1, UGT1A6, CXCL13, CXCL5, CXCL14, IL5RA, TNFRSF19, CSF3R, S100A9, S100A8, and S100A12) were selected and verified by RT-qPCR. Differences in DMD, FASN, and MMP7 were also confirmed at a protein level. Analysis of scRNA-Seq was used to trace their cellular origin to determine which lung cells regulated them. The principal cell sources of DMD were ciliated cells, alveolar type I/II epithelial cells (AT cells), club cells, and alveolar macrophages (AMs); MMP7 derives from AT cells, club cells, and AMs, while FASN originates from AT cells, ciliated cells, and AMs. Conclusion: Our data revealed a comprehensive transcriptional mRNA profile of IPF and demonstrated that ECM remodeling, lipid metabolism, and immune effect were collaboratively involved in the early development of IPF.

Comparison of machine learning tools for the prediction of AMD based on genetic, age, and diabetes-related variables in the Chinese population.

INTRODUCTION: Age-related macular degeneration (AMD) is the main cause of visual impairment and the most important cause of blindness in older people. However, there is currently no effective treatment for this disease, so it is necessary to establish a risk model to predict AMD development. METHODS: This study included a total of 202 subjects, comprising 82 AMD patients and 120 control subjects. Sixty-six single-nucleotide polymorphisms (SNPs) were identified using the MassArray assay. Considering 14 independent clinical variables as well as SNPs, four predictive models were established in the training set and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUROC). The difference distributions of the 14 independent clinical features between the AMD and control groups were tested using the chi-squared test. Age and diabetes were adjusted using logistic regression analysis and the "genomic-control" method was used for multiple testing correction. RESULTS: Three SNPs (rs10490924, OR = 1.686, genomic-control corrected p-value (GC) = 0.030; rs2338104, OR = 1.794, GC = 0.025 and rs1864163, OR = 2.125, GC = 0.038) were significant risk factors for AMD development. In the training set, four models obtained AUROC values above 0.72. CONCLUSIONS: We believe machine learning tools will be useful for the early prediction of AMD and for the development of relevant intervention strategies.