Suppr超能文献

糖尿病患者慢性肾病潜在生物标志物的识别:一项横断面观察性研究方案

Identification of Potential Biomarkers of Chronic Kidney Disease in Individuals with Diabetes: Protocol for a Cross-sectional Observational Study.

作者信息

Lecamwasam Ashani R, Mohebbi Mohammadreza, Ekinci Elif I, Dwyer Karen M, Saffery Richard

机构信息

Epigenetics Research, Murdoch Children's Research Institute, Victoria, Australia.

Department of Endocrinology, Austin Health, Victoria, Australia.

出版信息

JMIR Res Protoc. 2020 Jul 31;9(7):e16277. doi: 10.2196/16277.

Abstract

BACKGROUND

The importance of identifying people with diabetes and progressive kidney dysfunction relates to the excess morbidity and mortality of this group. Rates of cardiovascular disease are much higher in people with both diabetes and kidney dysfunction than in those with only one of these conditions. By the time these people are identified in current clinical practice, proteinuria and renal dysfunction are already established, limiting the effectiveness of therapeutic interventions. The identification of an epigenetic or blood metabolite signature or gut microbiome profile may identify those with diabetes at risk of progressive chronic kidney disease, in turn providing targeted intervention to improve patient outcomes.

OBJECTIVE

This study aims to identify potential biomarkers in people with diabetes and chronic kidney disease (CKD) associated with progressive renal injury and to distinguish between stages of chronic kidney disease. Three sources of biomarkers will be explored, including DNA methylation profiles in blood lymphocytes, the metabolomic profile of blood-derived plasma and urine, and the gut microbiome.

METHODS

The cross-sectional study recruited 121 people with diabetes and varying stages (stages 1-5) of chronic kidney disease. Single-point data collection included blood, urine, and fecal samples in addition to clinical data such as anthropometric measurements and biochemical parameters. Additional information obtained from medical records included patient demographics, medical comorbidities, and medications.

RESULTS

Data collection commenced in January 2018 and was completed in June 2018. At the time of submission, 121 patients had been recruited, and 119 samples remained after quality control. There were 83 participants in the early diabetes-associated CKD group with a mean estimated glomerular filtration rate (eGFR) of 61.2 mL/min/1.73 m2 (early CKD group consisting of stage 1, 2, and 3a CKD), and 36 participants in the late diabetic CKD group with a mean eGFR of 23.9 mL/min/1.73 m2 (late CKD group, consisting of stage 3b, 4, and 5), P<.001. We have successfully obtained DNA for methylation and microbiome analyses using the biospecimens collected via this protocol and are currently analyzing these results together with the metabolome of this cohort of individuals with diabetic CKD.

CONCLUSIONS

Recent advances have improved our understanding of the epigenome, metabolomics, and the influence of the gut microbiome on the incidence of diseases such as cancers, particularly those related to environmental exposures. However, there is a paucity of literature surrounding these influencers in renal disease. This study will provide insight into the fundamental understanding of the pathophysiology of CKD in individuals with diabetes, especially in novel areas such as epigenetics, metabolomics, and the kidney-gut axis.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16277.

摘要

背景

识别患有糖尿病和进行性肾功能不全的人群具有重要意义,因为这一群体的发病率和死亡率较高。糖尿病和肾功能不全患者的心血管疾病发生率远高于仅患其中一种疾病的患者。在当前临床实践中识别出这些患者时,蛋白尿和肾功能不全已经存在,这限制了治疗干预的效果。识别表观遗传或血液代谢物特征或肠道微生物群谱可能会识别出有进展为慢性肾病风险的糖尿病患者,从而提供有针对性的干预措施以改善患者预后。

目的

本研究旨在识别糖尿病和慢性肾病(CKD)患者中与进行性肾损伤相关的潜在生物标志物,并区分慢性肾病的不同阶段。将探索三种生物标志物来源,包括血液淋巴细胞中的DNA甲基化谱、血液来源的血浆和尿液的代谢组学谱以及肠道微生物群。

方法

这项横断面研究招募了121名患有糖尿病且处于慢性肾病不同阶段(1 - 5期)的患者。单点数据收集包括血液、尿液和粪便样本,以及人体测量和生化参数等临床数据。从医疗记录中获得的其他信息包括患者人口统计学、合并症和用药情况。

结果

数据收集于2018年1月开始,2018年6月完成。在提交本文时,已招募121名患者,经质量控制后剩余119个样本。糖尿病相关早期CKD组有83名参与者,平均估计肾小球滤过率(eGFR)为61.2 mL/min/1.73 m²(早期CKD组包括1、2和3a期CKD),糖尿病晚期CKD组有36名参与者,平均eGFR为23.9 mL/min/1.73 m²(晚期CKD组包括3b、4和5期),P <.001。我们已成功使用通过该方案收集的生物样本获得用于甲基化和微生物组分析的DNA,目前正在分析这些结果以及该糖尿病CKD患者队列的代谢组。

结论

最近的进展增进了我们对表观基因组、代谢组学以及肠道微生物群对癌症等疾病发病率影响的理解,尤其是那些与环境暴露相关的疾病。然而,关于这些影响因素在肾脏疾病方面的文献较少。本研究将为深入理解糖尿病患者CKD的病理生理学提供见解,特别是在表观遗传学、代谢组学和肾 - 肠轴等新领域。

国际注册报告识别号(IRRID):DERR1 - 10.2196/16277。

相似文献

7
The German Chronic Kidney Disease (GCKD) study: design and methods.德国慢性肾脏病(GCKD)研究:设计与方法。
Nephrol Dial Transplant. 2012 Apr;27(4):1454-60. doi: 10.1093/ndt/gfr456. Epub 2011 Aug 22.
10
The kidney and cardiovascular outcome trials.肾脏和心血管结局试验。
J Diabetes. 2018 Feb;10(2):88-89. doi: 10.1111/1753-0407.12616.

本文引用的文献

4
Metabolomics and renal disease.代谢组学与肾脏疾病
Curr Opin Nephrol Hypertens. 2015 Jul;24(4):371-9. doi: 10.1097/MNH.0000000000000136.
5
Diabetic nephropathy--emerging epigenetic mechanisms.糖尿病肾病——新兴的表观遗传机制。
Nat Rev Nephrol. 2014 Sep;10(9):517-30. doi: 10.1038/nrneph.2014.116. Epub 2014 Jul 8.
6
PEAR: a fast and accurate Illumina Paired-End reAd mergeR.PEAR:一种快速而准确的 Illumina 双端读取合并器。
Bioinformatics. 2014 Mar 1;30(5):614-20. doi: 10.1093/bioinformatics/btt593. Epub 2013 Oct 18.
8
Chronic kidney disease alters intestinal microbial flora.慢性肾脏病改变肠道微生物菌群。
Kidney Int. 2013 Feb;83(2):308-15. doi: 10.1038/ki.2012.345. Epub 2012 Sep 19.
9
UCHIME improves sensitivity and speed of chimera detection.UCHIME 提高了嵌合体检测的灵敏度和速度。
Bioinformatics. 2011 Aug 15;27(16):2194-200. doi: 10.1093/bioinformatics/btr381. Epub 2011 Jun 23.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验