Focus on acute kidney injury

Potential therapeutic applications of circular RNA in acute kidney injury

Acute kidney injury (AKI) is a common clinical syndrome characterized by a rapid deterioration in renal function, manifested by a significant increase in creatinine and a sharp decrease in urine output. The incidence of morbidity and mortality associated with AKI is on the rise, with most patients progressing to chronic kidney disease or end-stage renal disease. Treatment options for patients with AKI remain limited. Circular RNA (circRNA) is a wide and diverse class of non-coding RNAs that are present in a variety of organisms and are involved in gene expression regulation. Studies have shown that circRNA acts as a competing RNA, is involved in disease occurrence and development, and has potential as a disease diagnostic and prognostic marker. CircRNA is involved in the regulation of important biological processes, including apoptosis, oxidative stress, and inflammation. This study reviews the current status and progress of circRNA research in the context of AKI.

Internal and external validation of machine learning-assisted prediction models for mechanical ventilation-associated severe acute kidney injury

BACKGROUND

Currently, very few preventive or therapeutic strategies are used for mechanical ventilation (MV)-associated severe acute kidney injury (AKI).

OBJECTIVES

We developed clinical prediction models to detect the onset of severe AKI in the first week of intensive care unit (ICU) stay during the initiation of MV.

METHODS

A large ICU database Medical Information Mart for Intensive Care IV (MIMIC-IV) was analysed retrospectively. Data were collected from the clinical information recorded at the time of ICU admission and during the initial 12 h of MV. Using univariate and multivariate analyses, the predictors were selected successively. For model development, two machine learning algorithms were compared. The primary goal was to predict the development of AKI stage 2 or 3 (AKI-23) and AKI stage 3 (AKI-3) in the first week of patients' ICU stay after initial 12 h of MV. The developed models were externally validated using another multicentre ICU database (eICU Collaborative Research Database, eICU) and evaluated in various patient subpopulations.

RESULTS

Models were developed using data from the development cohort (MIMIC-IV: 2008-2016; n = 3986); the random forest algorithm outperformed the logistic regression algorithm. In the internal (MIMIC-IV: 2017-2019; n = 1210) and external (eICU; n = 1494) validation cohorts, the incidences of AKI-23 were 154 (12.7%) and 119 (8.0%), respectively, with areas under the receiver operator characteristic curve of 0.78 (95% confidence interval [CI]: 0.74-0.82) and 0.80 (95% CI: 0.76-0.84); the incidences of AKI-3 were 81 (6.7%) and 67 (4.5%), with areas under the receiver operator characteristic curve of 0.81 (95% CI: 0.76-0.87) and 0.80 (95% CI: 0.73-0.86), respectively.

CONCLUSIONS

Models driven by machine learning and based on routine clinical data may facilitate the early prediction of MV-associated severe AKI. The validated models can be found at: https://apoet.shinyapps.io/mv_aki_2021_v2/.

Farnesoid X receptor promotes renal ischaemia-reperfusion injury by inducing tubular epithelial cell apoptosis

Purpose

We investigated the role of farnesoid X receptor (FXR), a ligand‐dependent transcription factor, in renal ischaemia‐reperfusion (I/R) injury.

Materials and Methods

We performed unilateral renal I/R model in FXR knockout (Fxr^−/−) and wild‐type (WT) mice in vivo and a hypoxia‐reoxygenation (H/R) model in vitro. The pathways by which FXR induces apoptosis were detected using a proteome profiler array. The effects of FXR on apoptosis were evaluated using immunoblotting, TUNEL assays and flow cytometry.

Results

Compared with WT mice, Fxr^−/− mice showed improved renal function and reduced tubular injury scores and apoptosis. Consistent with the in vivo results, the silencing of FXR decreased the number of apoptotic HK‐2 cells after H/R, while FXR overexpression aggravated apoptosis. Notably, bone marrow transplantation (BMT) and immunohistochemistry experiments revealed the involvement of FXR in the tubular epithelium rather than in inflammatory cells. Furthermore, in vivo and in vitro studies demonstrated that FXR deficiency increased phosphorylated Bcl‐2 agonist of cell death (p‐Bad) expression levels and the ratio of Bcl‐2/Bcl‐xL to Bax expression in the kidney. Treatment with wortmannin, which reduced p‐Bad expression, inhibited the effects of FXR deficiency and eliminated the tolerance of Fxr^−/− mouse kidneys to I/R injury.

Conclusions

These results established the pivotal importance of FXR inactivation in tubular epithelial cells after I/R injury. FXR may promote the apoptosis of renal tubular epithelial cells by inhibiting PI3k/Akt‐mediated Bad phosphorylation to cause renal I/R damage.

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model

Background

Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients’ outcomes and data mining is such a powerful prediction tool, many AKI prediction models based on machine learning methods have been proposed. Our motivation is inspired by the fact that the incidence of AKI is a changing temporal sequence affected by the joint action of patients’ daily drug combinations and their physiological indexes. However, most existing models have not considered such a temporal correlation. Besides, due to great challenges caused by sparse, high-dimensional and highly imbalanced clinical data, it is hard to achieve ideal performance.

Methods

We develop a fast, simple and less-costly model based on an ensemble learning algorithm, named Ensemble Time Series Model (ETSM). Besides benefiting from vital signs and laboratory results as explicit indicators, ETSM explores the effect of drug combinations as possible implicit indicators for the AKI prediction. The model transforms temporal medication information into a multidimensional vector to consider and measure drug cumulative effects that may cause AKI.

Results

We compare ETSM with state-of-the-art models on ICUC and MIMIC III datasets. On the basis of the experimental results, our model obtains satisfactory performance (ICUC: AUC 24 hours ahead: 0.81, 48 hours ahead: 0.78; MIMIC III: AUC 24 hours ahead: 0.95, 48 hours ahead: 0.95). Meanwhile, we compare the effects of different sampling and feature generation methods on the model performance. In the ablation study, we validate that medication information improves model performance (24 hours ahead: AUC increased from 0.74 to 0.81). We also find that the model’s performance is closely related to the balanced level of the derivation dataset. The optimal ratio of major class size to minor class size for the model is found for AKI prediction.

Conclusions

ETSM is an effective method for the early prediction of AKI. The model verifies that AKI incidence is related to the clinical medication. In comparison with other prediction methods, ETSM provides comparable performance results and better interpretability.

The interaction between C/EBPβ and TFAM promotes acute kidney injury via regulating NLRP3 inflammasome-mediated pyroptosis

Sepsis-induced inflammatory damage is a crucial cause of acute kidney injury (AKI), and AKI is an ecumenical fearful complication in approximately half of patients with sepsis. CCAAT/enhancer-binding protein β (C/EBPβ) plays roles in regulating acute phase responses and inflammation. However, the role and mechanism of C/EBPβ in AKI are unclear. LPS combined with ATP-treated renal epithelial cells HK2 and cecal ligation-peferation (CLP)-mice were used as models of AKI in vitro and in vivo. Cell damage, the secretion of interleukin-1 beta (IL-1β), IL-18 and cysteinyl aspartate specific proteinase 1 (caspase-1) activity were tested by LDH, ELISA assay and flow cytometry analysis, respectively. The expression levels of TFAM, C/EBPβ, and pyroptosis-related molecules were tested by qRT-PCR and Western blotting. Chromatin immunoprecipitation (ChIP) assessed the interaction between C/EBPβ with TFAM. Hematoxylin-Eosin (H&E) staining detected pathological changes of kidney tissues, and immunohistochemistry measured TFAM and C/EBPβ in mice kidney tissues. C/EBPβ or TFAM were up-regulated in LPS combined with ATP -induced HK2 cells. Knockdown of C/EBPβ could suppress cell injury and the secretion of IL-1β and IL-18 induced by LPS combined with ATP. Furthermore, C/EBPβ up-regulated the expression levels of TFAM via directly binding to TFAM promoter. Overexpression of TFAM reversed the effects of C/EBPβ deficiency on pyroptosis. Knockdown of C/EBPβ could inhibit NLRP3 inflammasome-mediated caspase-1 signaling pathway by inactivating TFAM/RAGE pathway. It was further confirmed in the AKI mice that C/EBPβ and TFAM promoted AKI by activating NLRP3-mediated pyroptosis. The interaction of between C/EBPβ and TFAM facilitated pyroptosis by activating NLRP3/caspase-1 signal axis, thereby promoting the occurrence of AKI.

Current understanding and future directions in the application of TIMP-2 and IGFBP7 in AKI clinical practice

NephroCheck® is the commercial name of a combined product of two urinary biomarkers, tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7), expressed as [TIMP-2]·[IGFBP7], used to identify patients at high risk of acute kidney injury (AKI). AKI is a common and harmful complication especially in critically-ill patients, which can induce devastating short- and long-term outcomes. Over the past decade, numerous clinical studies have evaluated the utility of several biomarkers (e.g. neutrophil gelatinase-associated lipocalin, interleukin-18, liver-type fatty acid binding protein and kidney injury molecule-1, cystatin C) in the early diagnosis and risk stratification of AKI. Among all these biomarkers, [TIMP-2]·[IGFBP7] was confirmed to be superior in early detection of AKI, before the decrease of renal function is evident. In 2014, the US Food and Drug Administration permitted marketing of NephroCheck® (Astute Medical) (measuring urinary [TIMP-2]·[IGFBP7]) to determine if certain critically-ill patients are at risk of developing moderate to severe AKI. It has since been applied to clinical work in many hospitals of the United States and Europe to improve the diagnostic accuracy and outcomes of AKI patients. Now, more and more research is devoted to the evaluation of its application value, meaning and method in different clinical settings. In this review, we summarize the current research status of [TIMP-2]·[IGFBP7] and point out its future directions.