Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment

Abstract

Background/aim: Hypertensive nephropathy (HN) is a common complication of hypertension. Traditional Chinese medicine has long been used in the clinical treatment of Hypertensive nephropathy. However, botanical drug prescriptions have not been summarized. The purpose of this study is to develop a prescription for improving hypertensive nephropathy, explore the evidence related to clinical application of the prescription, and verify its molecular mechanism of action. Methods: In this study, based on the electronic medical record data on Hypertensive nephropathy, the core botanical drugs and patients' symptoms were mined using the hierarchical network extraction and fast unfolding algorithm, and the protein interaction network between botanical drugs and Hypertensive nephropathy was established. The K-nearest neighbors (KNN) model was used to analyze the clinical and biological characteristics of botanical drug compounds to determine the effective compounds. Hierarchical clustering was used to screen for effective botanical drugs. The clinical efficacy of botanical drugs was verified by a retrospective cohort. Animal experiments were performed at the target and pathway levels to analyze the mechanism. Results: A total of 14 botanical drugs and five symptom communities were obtained from real-world clinical data. In total, 76 effective compounds were obtained using the K-nearest neighbors model, and seven botanical drugs were identified as Gao Shen Formula by hierarchical clustering. Compared with the classical model, the Area under the curve (AUC) value of the K-nearest neighbors model was the best; retrospective cohort verification showed that Gao Shen Formula reduced serum creatinine levels and Chronic kidney disease (CKD) stage [OR = 2.561, 95% CI (1.025-6.406), p < 0.05]. With respect to target and pathway enrichment, Gao Shen Formula acts on inflammatory factors such as TNF-α, IL-1β, and IL-6 and regulates the NF-κB signaling pathway and downstream glucose and lipid metabolic pathways. Conclusion: In the retrospective cohort, we observed that the clinical application of Gao Shen Formula alleviates the decrease in renal function in patients with hypertensive nephropathy. It is speculated that Gao Shen Formula acts by reducing inflammatory reactions, inhibiting renal damage caused by excessive activation of the renin-angiotensin-aldosterone system, and regulating energy metabolism.

Keywords: NF-κB signal pathway; clinical decision support; hypertensive nephropathy; machine learning; real-world data.

FIGURE 1

Workflow of botanical drugs screening and efficacy verification. (A) Establishing symptom–botanical drug relationships. Based on the symptom network and protein–protein interaction network, we quantified the biological characteristics of botanical drugs. (B) Analysis of botanical drug characteristics. The KNN model was used to comprehensively analyze the biological characteristics of botanical drugs to form a correlation heatmap of the interaction between botanical drugs. After hierarchical clustering algorithm screening, the composition of the Gao Shen Formula is determined. (C) Regression cohort validation. We screened patients based on the inclusion and exclusion criteria. After propensity score matching, the exposed group and the non-exposed group each included 91 patients. (D) Experimental model verification. We used Gao Shen Formula, irbesartan, and saline to receive SHR or WKY for 8 weeks, and observed the differences of blood pressure, echocardiography, kidney, and blood indexes in rats.

FIGURE 2

Data collation from EMRs. (A) Network map of symptom distribution in patients with HN. (B) Core nodes of the PPIN.

FIGURE 3

Results obtained using the machine learning model. (A) ROC curves obtained using the KNN model. (B) Hierarchical clustering of botanical drugs. (C) ROC curves obtained using each comparison model.

FIGURE 4

Characteristics of the GSF pathway. (A) Target characteristics of GSF. (B) GSF and HN interactive network. (C) and (D) are the KEGG pathway and GO enrichment heatmaps of the GSF and disease modules. In the map, the average number of enriched genes is 0; numbers higher than the average are shown in red, and numbers lower than the average are shown in blue.

FIGURE 5

Mechanism of action of GSF. (A) HE staining of kidneys at ×200 magnification. (B) Expression of NF-κB p65 in the kidney. (C) GSF regulates the NF-κB signaling pathway; it can interfere with the production of the IKK complex in the NF-κB pathway and inhibit NF-κB protein phosphorylation by regulating TNF-α, IL-1β, and PKC. At the same time, GSF can also reduce AP1 phosphorylation and reduce the production of inflammatory factors by affecting MAPK8. (D) GSF regulates the downstream inflammatory reaction, regulates the NF-κB pathway, reduces the secretion of inflammatory factors, and improves IRS-1 activity, insulin resistance, and lipid metabolism directly or indirectly by inhibiting the IKK-β complex.