Analysis of blood microbiota in patients with adult-onset Still's disease and sepsis by metagenomic next-generation sequencing.

Aims/Background Adult-onset Still's disease (AOSD) shares similar clinical symptoms with sepsis. Thus, differentiating between AOSD and sepsis presents a great challenge while making diagnosis. This study aimed to analyse the changes in blood microbiota related to AOSD and sepsis using metagenomic next-generation sequencing (mNGS), identify potential biomarkers that distinguish AOSD from sepsis, and explore the diagnostic value of mNGS in differentiation between these two pathological conditions. Methods Clinical data of four AOSD patients and four sepsis patients treated in the Department of Rheumatology and Immunology, The Affiliated Hospital of Xuzhou Medical University between October 2021 and February 2022 were collected. The mNGS diagnostic records of these patients were analysed for microbial correlations in terms of species taxonomic structure and beta diversity by comparing blood microbiota between AOSD and sepsis. The biomarkers with the strongest capability in distinguishing the subgroups were screened using a random forest algorithm. Results There was no statistically significant differences between AOSD patients and sepsis controls in terms of gender and age (p > 0.05). A total of 91 operational taxonomic units (OTUs) were obtained. At the level of phylum, Proteobacteria, Ascomycota and Basidiomycota were present in high abundances in both groups (79.76%, 14.18% and 3.30% vs 54.03%, 32.77% and 5.81%). At the genus level, the abundances of Parainfluenzae, Aspergillus and Ralstonia were the top three highest in the AOSD group (73.88%, 10.92% and 5.48%), while Ralstonia, Aspergillus and Malassezia were ranked as the top three in the sepsis group in term of abundance (48.69%, 27.36% and 5.52%). In beta-diversity analysis, there were advances shown in visual principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) between the AOSD group and sepsis group (p < 0.05), with little significant differences in the analysis of similarities (Anosim) (p > 0.05). Linear discriminant analysis effect size (LEfSe) showed that Mucoromycota, Saccharomycetes, Moraxellales, Mucorales, Xanthomonadales, Saccharomycetales, Acinetobacter, Stenotrophomonas, Yarrowia, Apophysomyces, Acinetobacter johnson, Yarrowia lipolytica, Apophysomyces variabilis and Stenotrophomonas maltophilia were more enriched in sepsis group (p < 0.05). The top five variables with the strongest capability in distinguishing between AOSD and sepsis were Acinetobacter johnsonii, Apophysomyces variabilis, Propionibacterium acnes, Stenotrophomonas maltophilia and Yarrowia lipolytica. Conclusion The blood microorganisms in AOSD were different from sepsis, and mNGS was potential to distinguish between AOSD and sepsis.

Expression and diagnostic value of interleukin-22 in rheumatoid arthritis-associated interstitial lung disease.

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by a high incidence and mortality rate, highlighting the need for biomarkers to detect ILD early in RA patients. Previous studies have shown the protective effects of Interleukin-22 (IL-22) in pulmonary fibrosis using mouse models. This study aims to assess IL-22 expression in RA-ILD to validate foundational experiments and explore its diagnostic value. The study included 66 newly diagnosed RA patients (33 with ILD, 33 without ILD) and 14 healthy controls (HC). ELISA was utilized to measure IL-22 levels and perform intergroup comparisons. The correlation between IL-22 levels and the severity of RA-ILD was examined. Logistic regression analysis was employed to screen potential predictive factors for RA-ILD risk and establish a predictive nomogram. The diagnostic value of IL-22 in RA-ILD was assessed using ROC. Subsequently, the data were subjected to 30-fold cross-validation. IL-22 levels in the RA-ILD group were lower than in the RA-No-ILD group and were inversely correlated with the severity of RA-ILD. Logistic regression analysis identified IL-22, age, smoking history, anti-mutated citrullinated vimentin antibody (MCV-Ab), and mean corpuscular hemoglobin concentration (MCHC) as independent factors for distinguishing between the groups. The diagnostic value of IL-22 in RA-ILD was moderate (AUC = 0.75) and improved when combined with age, smoking history, MCV-Ab and MCHC (AUC = 0.97). After 30-fold cross-validation, the average AUC was 0.886. IL-22 expression is dysregulated in the pathogenesis of RA-ILD. This study highlights the potential of IL-22, along with other factors, as a valuable biomarker for assessing RA-ILD occurrence and progression.

Caspase-11/GSDMD contributes to the progression of hyperuricemic nephropathy by promoting NETs formation.

Hyperuricemia is an independent risk factor for chronic kidney disease (CKD) and promotes renal fibrosis, but the underlying mechanism remains largely unknown. Unresolved inflammation is strongly associated with renal fibrosis and is a well-known significant contributor to the progression of CKD, including hyperuricemia nephropathy. In the current study, we elucidated the impact of Caspase-11/Gasdermin D (GSDMD)-dependent neutrophil extracellular traps (NETs) on progressive hyperuricemic nephropathy. We found that the Caspase-11/GSDMD signaling were markedly activated in the kidneys of hyperuricemic nephropathy. Deletion of Gsdmd or Caspase-11 protects against the progression of hyperuricemic nephropathy by reducing kidney inflammation, proinflammatory and profibrogenic factors expression, NETs generation, α-smooth muscle actin expression, and fibrosis. Furthermore, specific deletion of Gsdmd or Caspase-11 in hematopoietic cells showed a protective effect on renal fibrosis in hyperuricemic nephropathy. Additionally, in vitro studies unveiled the capability of uric acid in inducing Caspase-11/GSDMD-dependent NETs formation, consequently enhancing α-smooth muscle actin production in macrophages. In summary, this study demonstrated the contributory role of Caspase-11/GSDMD in the progression of hyperuricemic nephropathy by promoting NETs formation, which may shed new light on the therapeutic approach to treating and reversing hyperuricemic nephropathy.

GSDMD and GSDME synergy in the transition of acute kidney injury to chronic kidney disease.

BACKGROUND AND HYPOTHESIS: Acute kidney injury (AKI) could progress to chronic kidney disease (CKD) and the AKI-CKD transition has major clinical significance. A growing body of evidence has unveiled the role of pyroptosis in kidney injury. We postulate that GSDMD and GSDME exert cumulative effects on the AKI-CKD transition by modulating different cellular responses. METHODS: We established an AKI-CKD transition model induced by folic acid in wildtype (WT), Gsdmd-/-, Gsdme-/-, and Gsdmd-/-Gsdme-/- mice. Tubular injury, renal fibrosis and inflammatory responses were evaluated. In vitro studies were conducted to investigate the interplay among tubular cells, neutrophils, and macrophages. RESULTS: Double deletion of Gsdmd and Gsdme conferred heightened protection against AKI, mitigating inflammatory responses, including the formation of neutrophil extracellular traps (NETs), macrophage polarization and differentiation, and ultimately renal fibrosis, compared with wildtype mice and mice with single deletion of either Gsdmd or Gsdme. Gsdme, but not Gsdmd deficiency, shielded tubular cells from pyroptosis. GSDME-dependent tubular cell death stimulated NETs formation and prompted macrophage polarization towards a pro-inflammatory phenotype. Gsdmd deficiency suppressed NETs formation and subsequently hindered NETs-induced macrophage-to-myofibroblast transition (MMT). CONCLUSION: GSDMD and GSDME collaborate to contribute to AKI and subsequent renal fibrosis induced by folic acid. Synchronous inhibition of GSDMD and GSDME could be an innovative therapeutic strategy for mitigating the AKI-CKD transition.

The association between serum magnesium and chronic kidney disease in Chinese adults: a cross-sectional study.

BACKGROUND: Magnesium (Mg) is both an essential macro-element and a known catalyst, and it plays a vital role in various physiological activities and mechanisms in relation to chronic kidney disease (CKD). However, epidemiological evidence involving this is limited and not entirely consistent. This study aims to explore the association of serum Mg concentrations with the risk of CKD among general Chinese adults. METHODS: A total of 8,277 Chinese adults were included in the wave of 2009 from the China Health and Nutrition Survey (CHNS). The primary outcome was the risk of CKD, which was defined as the estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2. Multivariable logistic regression model was used to examine the relationship of serum Mg concentrations with the risk of CKD. RESULTS: Included were 8,277 individuals, with an overall CKD prevalence of 11.8% (n = 977). Compared with the first quartile of serum Mg, the multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for participants in the second, third, and fourth quartiles of serum Mg were 0.74 (0.58, 0.93), 0.87 (0.69, 1.11) and 1.29 (1.03, 1.61), respectively. Similar results were observed in our several sensitivity analyses. Restricted cubic spline analysis demonstrated a nonlinear (similar "J"-shaped) association between serum Mg concentrations and the risk of CKD (Pnonlinearity <0.001), with a threshold at around a serum Mg value of 2.2 mg/dL. CONCLUSIONS: Our results suggested a similar "J"-shaped association between serum Mg concentration and the risk of CKD among Chinese adults. Further large prospective studies are needed to verify these findings.

Identification of discriminatory factors and construction of nomograms for differentiating AOSD and sepsis.

OBJECTIVE: This study aimed to develop nomogram prediction models to differentiate between adult-onset Still's disease (AOSD) and sepsis. METHODS: We retrospectively collected laboratory test data from 107 hospitalized patients with AOSD and sepsis at the Affiliated Hospital of Xuzhou Medical University. Multivariate binary logistic regression was used to develop nomogram models using arthralgia, WBC, APTT, creatinine, PLT, and ferritin as independent factors. The performance of the model was evaluated by the bootstrap consistency index and calibration curve. RESULTS: Model 1 had an AUC of 0.98 (95% CI, 0.96-1.00), specificity of 0.98, and sensitivity of 0.94. Model 2 had an AUC of 0.96 (95% CI, 0.93-1.00), specificity of 0.92, and sensitivity of 0.94. The fivefold cross-validation yielded an accuracy (ACC) of 0.92 and a kappa coefficient of 0.83 for Model 1, while for Model 2, the ACC was 0.87 and the kappa coefficient was 0.74. CONCLUSION: The nomogram models developed in this study are useful tools for differentiating between AOSD and sepsis. Key Points • The differential diagnosis between AOSD and sepsis has always been a challenge • Delayed treatment of AOSD may lead to serious complications • We developed two nomogram models to distinguish AOSD from sepsis, which were not previously reported • Our models can be used to guide clinical practice with good discrimination.

Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm.

OBJECTIVE: The differential diagnosis between adult-onset Still's disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. METHODS: All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. RESULTS: The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still's disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . CONCLUSION: We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Identification of new immune subtypes of renal injury associated with anti-neutrophil cytoplasmic antibody-associated vasculitis based on integrated bioinformatics analysis.

Background: Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that may lead to end-stage renal disease. However, few specifific biomarkers are available for AAV-related renal injury. The aim of this study was to identify important biomarkers and explore new immune subtypes of AAV-related renal injury. Methods: In this study, messenger RNA expression profiles for antibody-associated vasculitis and AAV-associated kidney injury were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes to AAV. Key module genes from WGCNA were then intersected with AAV- and nephropathy-related genes from the Genecards database to identify key genes for AAV-associated kidney injury. Subsequently, the expression of key genes was validated in independent datasets and the correlation of genes with clinical traits of kidney injury was verified by the Nephroseq database. Finally, non-negative matrix factorization (NMF) clustering was performed to identify the immune subtypes associated with the key genes. Results: Eight co-key genes (AGTR2, ANPTL2, BDKRB1, CSF2, FGA, IL1RAPL2, PCDH11Y, and PGR) were identifified, and validated the expression levels independent datasets. Receiver operating characteristic curve analysis revealed that these eight genes have major diagnostic value as potential biomarkers of AAV-related renal injury. Through our comprehensive gene enrichment analyses, we found that they are associated with immune-related pathways. NMF clustering of key genes identified two and three immune-related molecular subtypes in the glomerular and tubular data, respectively. A correlation analysis with prognostic data from the Nephroseq database indicated that the expression of co-key genes was positively co-related with the glomerular filtration rate. Discussion: Altogether, we identifified 8 valuable biomarkers that firmly correlate with the diagnosis and prognosis of AAV-related renal injury. These markers may help identify new immune subtypes for AAV-related renal injury.

Risk factors for renal failure and short-term prognosis in patients with spontaneous intracerebral haemorrhage complicated by acute kidney injury.

BACKGROUND: Although acute kidney injury (AKI) is a known risk factor for adverse clinical outcomes in patients with spontaneous intracerebral haemorrhage (SICH), little is known about the predisposing factors that contribute to renal failure and short-term prognosis in the setting of SICH already complicated by AKI. In this study, we aimed to identify the renal failure factors in SICH patents with AKI. METHODS: Five hundred forty-three patients with SICH complicated by differential severities of AKI who were admitted to the First Affiliated Hospital of Fujian Medical University from January 2016 to December 2018 were retrospectively studied. Logistic regression and receiver operator characteristic (ROC) curve analysis were performed to determine the best predictive and discriminative variables. Multivariate Cox regression analysis was performed to identify prognostic factors for renal recovery. RESULTS: In the multivariable adjusted model, we found that hypernatremia, metabolic acidosis, elevated serum creatine kinase, hyperuricaemia, proteinuria, and the use of colloids and diuretics were all independent risk factors for the occurrence of stage 3 AKI in SICH patients. The area under the curve analysis indicated that hypernatremia and hyperuricaemia were predictive factors for stage 3 AKI, and the combination of these two parameters increased their predictability for stage 3 AKI. Kaplan-Meier survival curves revealed that the renal recovery rate in SICH patients with stages 1 and 2 AKI was significantly higher than that in SICH patients with stage 3 AKI. Multivariate Cox regression analysis suggested that hypernatremia and the occurrence of stage 3 AKI are predictors for poor short-term renal recovery. CONCLUSIONS: These findings illustrate that hypernatremia and hyperuricaemia represent potential risk factors for the occurrence of stage 3 AKI in SICH patients. Those patients with hypernatremia and stage 3 AKI were associated with a poor short-term prognosis in renal recovery.