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Deciphering the Efficacy and Mechanisms of Chinese Herbal Medicine for Diabetic Kidney Disease by Integrating Web-Based Biochemical Databases and Real-World Clinical Data: Retrospective Cohort Study

机译:通过整合Web的生化数据库和现实世界临床数据来解密中草药对糖尿病肾疾病的疗效和机制:回顾性队列研究

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Background Diabetic kidney disease (DKD) is one of the most crucial causes of chronic kidney disease (CKD). However, the efficacy and biomedical mechanisms of Chinese herbal medicine (CHM) for DKD in clinical settings remain unclear. Objective This study aimed to analyze the outcomes of DKD patients with CHM-only management and the possible molecular pathways of CHM by integrating web-based biomedical databases and real-world clinical data. Methods A total of 152,357 patients with incident DKD from 2004 to 2012 were identified from the National Health Insurance Research Database (NHIRD) in Taiwan. The risk of mortality was estimated with the Kaplan-Meier method and Cox regression considering demographic covariates. The inverse probability of treatment weighting was used for confounding bias between CHM users and nonusers. Furthermore, to decipher the CHM used for DKD, we analyzed all CHM prescriptions using the Chinese Herbal Medicine Network (CMN), which combined association rule mining and social network analysis for all CHM prescriptions. Further, web-based biomedical databases, including STITCH, STRING, BindingDB, TCMSP, TCM@Taiwan, and DisGeNET, were integrated with the CMN and commonly used Western medicine (WM) to explore the differences in possible target proteins and molecular pathways between CHM and WM. An application programming interface was used to assess these online databases to obtain the latest biomedical information. Results About 13.7% (20,947/131,410) of patients were classified as CHM users among eligible DKD patients. The median follow-up duration of all patients was 2.49 years. The cumulative mortality rate in the CHM cohort was significantly lower than that in the WM cohort (28% vs 48%, P .001). The risk of mortality was 0.41 in the CHM cohort with covariate adjustment (99% CI 0.38-0.43; P .001). A total of 173,525 CHM prescriptions were used to construct the CMN with 11 CHM clusters. CHM covered more DKD-related proteins and pathways than WM; nevertheless, WM aimed at managing DKD more specifically. From the overrepresentation tests carried out by the online website Reactome, the molecular pathways covered by the CHM clusters in the CMN and WM seemed distinctive but complementary. Complementary effects were also found among DKD patients with concurrent WM and CHM use. The risk of mortality for CHM users under renin-angiotensin-aldosterone system (RAAS) inhibition therapy was lower than that for CHM nonusers among DKD patients with hypertension (adjusted hazard ratio [aHR] 0.47, 99% CI 0.45-0.51; P .001), chronic heart failure (aHR 0.43, 99% CI 0.37-0.51; P .001), and ischemic heart disease (aHR 0.46, 99% CI 0.41-0.51; P .001). Conclusions CHM users among DKD patients seemed to have a lower risk of mortality, which may benefit from potentially synergistic renoprotection effects. The framework of integrating real-world clinical databases and web-based biomedical databases could help in exploring the roles of treatments for diseases.
机译:背景技术糖尿病肾病(DKD)是慢性肾病(CKD)最重要的原因之一。然而,临床环境中,中草药(CHM)对DKD的疗效和生物医学机制仍然尚不清楚。目的本研究旨在通过整合基于网络的生物医学数据库和现实世界的临床数据来分析唯一谦逊的管理患者和CHM可能的分子途径的结果。方法从2004年至2012年达到2004年至2012年的152,357名患者,来自台湾国家健康保险研究数据库(纳希尔)。考虑人口协变量,卡普兰 - 梅尔方法和考克斯回归估计死亡风险。治疗加权的逆概率用于CHM用户和非用户之间的混杂偏差。此外,为了破译用于DKD的CHM,我们使用中草药网络(CMN)分析了所有CHM处方,其中组合协会规则挖掘和所有CHM处方的社交网络分析。此外,基于Web的生物医学数据库,包括针脚,弦,BINDINGDB,TCMSP,TCM @台湾和剥夺药物,与CMN和常用的西药(WM)集成,以探讨可能的靶标蛋白和CHM之间的分子途径的差异和wm。应用程序编程接口用于评估这些在线数据库以获得最新的生物医学信息。结果约有13.7%(20,947 / 131,410)患者被归类为符合条件的DKD患者中的CHM用户。所有患者的中位后续时间为2.49年。 CHM队列中的累积死亡率显着低于WM队列(28%Vs 48%,P& .001)。 CHM队列中死亡率的风险为0.41,具有相变调节(99%CI 0.38-0.43; P <.001)。共使用173,525个CHM处方来构建具有11个CHM簇的CMN。 CHM涵盖更多DKD相关的蛋白质和途径而不是WM;然而,WM旨在更具体地管理DKD。从在线网站进行的超级陈述测试中,CHM簇在CMN和WM中覆盖的分子途径似乎具有独特但互补。在DKD患者中也发现了互补效果,并同时使用CHM和CHM使用。肾素 - 血管紧张素 - 醛固酮系统(RAAS)抑制治疗下的CHM用户的死亡风险低于高血压患者的CHM非用途(调整后危险比[AHR] 0.47,99%CI 0.45-0.51; P& .001),慢性心力衰竭(AHR 0.43,99%CI 0.37-0.51; P& .001)和缺血性心脏病(AHR 0.46,99%CI 0.41-0.51; P <.001)。结论DKD患者中的CHM用户似乎具有较低的死亡风险,这可能会受益于潜在的协同效果效应。整合现实世界临床数据库和基于网络的生物医学数据库的框架可以有助于探索治疗疾病的角色。

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