Characteristics and function of the gut microbiota in patients with IgA nephropathy via metagenomic sequencing technology.

OBJECTIVE: The aim of this study was to investigate the characteristics and related functional pathways of the gut microbiota in patients with IgA nephropathy (IgAN) through metagenomic sequencing technology. METHODS: We enrolled individuals with primary IgAN, including patients with normal and abnormal renal function. Additionally, we recruited healthy volunteers as the healthy control group. Stool samples were collected, and species and functional annotation were performed through fecal metagenome sequencing. We employed linear discriminant analysis effect size (LEfSe) analysis to identify significantly different bacterial microbiota and functional pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to annotate microbiota functions, and redundancy analysis (RDA) was performed to analyze the factors affecting the composition and distribution of the gut microbiota. RESULTS: LEfSe analysis revealed differences in the gut microbiota between IgAN patients and healthy controls. The characteristic microorganisms in the IgAN group were classified as Escherichia coli, with a significantly greater abundance than that in the healthy control group (p < 0.05). The characteristic microorganisms in the IgAN group with abnormal renal function were identified as Enterococcaceae, Moraxella, Moraxella, and Acinetobacter. KEGG functional analysis demonstrated that the functional pathways of the microbiota that differed between IgAN patients and healthy controls were related primarily to bile acid metabolism. CONCLUSIONS: The status of the gut microbiota is closely associated not only with the onset of IgAN but also with the renal function of IgAN patients. The characteristic gut microbiota may serve as a promising diagnostic biomarker and therapeutic target for IgAN.

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.

OBJECTIVES: This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms. METHODS: Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP). RESULTS: A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis. CONCLUSIONS: The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.