Mendelian randomization study of sodium-glucose cotransporter 2 inhibitors in cardiac and renal diseases.

OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) target the reabsorption of sodium and glucose in the kidney proximal tubules to reduce blood sugar levels. However, clinical randomized controlled trials on SGLT2i have yielded inconsistent results, necessitating further research into their efficacy and safety for specific cardiac and renal diseases. METHODS: "Sodium in urine" was selected as a downstream biomarker of SGLT2i. Single nucleotide polymorphisms were extracted from genome-wide association study data as instrumental variables. Mendelian randomization analysis was then conducted for cardiac and renal diseases and potential adverse events. The causal effects of SGLT2i on these diseases were determined based on inverse variance weighted results, followed by sensitivity and pleiotropy tests. RESULTS: SGLT2i had a significant protective effect against nephrotic syndrome (odds ratio [OR] 0.0011, 95% confidence interval [CI] 0.000-0.237), chronic glomerulonephritis (OR 0.0002, 95% CI 0.000-0.21), and hypertensive nephropathy (OR 0.0003, 95% CI 0.000-0.785). No causal effects were observed between SGLT2i and cardiac diseases or potential adverse events. CONCLUSIONS: SGLT2i can act as protective factors against nephrotic syndrome, chronic glomerulonephritis, and hypertensive nephropathy.

Machine learning model to estimate probability of remission in patients with idiopathic membranous nephropathy.

BACKGROUND: Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions. METHODS: A total of 266 patients with histopathology-proven IMN were included in this study. Least absolute shrinkage and selection operator regression was utilized to identify the most important variables. Subsequently, multivariate Cox regression analysis was conducted to construct a nomogram, and bootstrap resampling was employed for internal validation. Receiver operating characteristic and calibration curves and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the developed model. RESULTS: A prognostic nomogram was established, which incorporated creatinine, glomerular_basement_membrane_thickening, gender, IgG_deposition, low-density lipoprotein cholesterol, and fibrinogen. The areas under the curves of the 3-, 12-, 24-month were 0.751, 0.725, and 0.830 in the training set, and 0.729, 0.730, and 0.948 in the validation set respectively. These results and calibration curves demonstrated the good discrimination and calibration of the nomogram in the training and validation sets. Additionally, DCA indicated that the nomogram was useful for remission prediction in clinical settings. CONCLUSION: The nomogram was useful for clinicians to evaluate the prognosis of patients with IMN in early stage.

Clinical predictive model to estimate probability of remission in patients with lupus nephritis.

BACKGROUND: Lupus nephritis (LN) is a major organ complication and cause of morbidity and mortality in patients with systemic lupus erythematosus. This study aims to provide the clinician with a quantitative tool for the prediction of the individual remission probability of LN and obtain new insights for improved clinical management in LN treatment. METHODS: A total of 301 patients with renal biopsy-proven LN were recruited and randomly divided into model construction and validation group. The least absolute shrinkage and selection operator regression analysis was conducted to select significant variables, and a multivariate Cox regression predictive model was established. The performance of the model was verified and tested with 1000-bootstrap validation in the validation group. Finally, the nomogram was constructed, and the performance was evaluated. The predictive accuracy and efficiency were verified through receiver operation characteristic and calibration curves. RESULTS: A total of 210 and 91 patients who all received renal biopsy were included in the training and validation group, respectively. A final prognostic model was established, which included the course of LN, gender, 24h-proteinuria, creatinine, triglycerides, FIB, Complement C3, anti-dsDNA antibody, tubular atrophy and classification of kidney biopsy. Moreover, an easy-to-use nomogram was built based on the predictive model. The areas under the curve (AUC) of the 1, 2, 5-year prediction were 77.12, 77.98 and 87.01 in the training group, respectively. In the validation group, the AUC of the 1, 2, 5-year prediction were 81.42, 87.20 and 92.81 respectively, which indicated good performance in predicting the remission probability of LN. CONCLUSION: This novel model was constructed to predict the remission probability of patients with LN for the first time. This model displayed good predictive performance and was easy to use for clinical practice.

Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis.

PURPOSE: Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data. METHODS: Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed. Least absolute shrinkage and selection operator regression analysis were conducted to establish a multivariate logistic regression model. A nomogram based on the multivariate logistic regression model was constructed for easy application in clinical practice. Concordance index, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram. RESULTS: The predictive factors contained in the multivariate model included duration, gender, respiratory infection, arthritis, edema, estimated glomerular filtration rate, 24 h urine protein, uric acid, and renal ultrasound intensity. The area under the curves (AUC) of the nomogram in the training set and testing set were 0.814 and 0.822, respectively, indicating its good predictive ability. Moreover, the DCA curve and CIC revealed its clinical utility. CONCLUSION: The developed multivariate predictive model combines the clinical and laboratory factors of patients with IgAVN and is useful in the individualized prediction of the 1-year remission probability aid for clinical decision-making during treatment and management of IgAVN.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

BACKGROUND: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST. METHODS: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. RESULTS: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively. CONCLUSIONS: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Bibliometric analysis of the 100 most-cited articles in the field of hepatology.

OBJECTIVE: Bibliometric search of citation classics can function as a tool to identify extraordinary landmark articles and advanced research studies. We aimed to examine and characterize the 100 most-cited published articles in the field of hepatology. PATIENTS AND METHODS: A comprehensive list of the 100 most-cited articles published from 1950 to 2017 in the field of hepatology was compiled after searching the Web of Science with relevant terms, including "liver," "hepatitis," "hepatic," "hepatocellular," "hepatology," "cirrhosis," and "steatohepatitis." The articles were ranked according to their citation counts and were evaluated for characteristics including country, institution, authorship, publication year, subspecialty and others. RESULTS: The database search returned 323,291 articles associated with liver disease published between 1950 and 2017. The 100 most-cited articles were from 21 major journals, with the highest number of articles being published in Hepatology (n=20). The average number of citations of the 100 most-cited articles was 1946.8; among these articles, the most frequently cited article received 5515 citations, and the least frequently cited article received 1155 citations. In total, 60 were original articles among the 100 most-cited articles. The most frequently represented specialties were hepatitis, hepatocellular carcinoma, and nonalcoholic fatty liver disease, which accounted for 53.3%, 23.3%, and 11.7% of these articles, respectively. DISCUSSION: Our study identified citation classics and provided a review of the most advanced studies in the field of hepatology. This can help to guide clinical treatment and future academic research resulting in advancements in hepatology.