Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen's University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB, Northern Ireland, UK.
Regional Nephrology Unit, Belfast City Hospital, Belfast, UK.
J Transl Med. 2018 Oct 25;16(1):292. doi: 10.1186/s12967-018-1664-7.
Chronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD.
Conventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management.
CKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases.
慢性肾脏病（CKD）是一个全球性的公共卫生问题，在老年人中更为普遍，并与多种合并症有关。糖尿病和高血压是 CKD 的常见病因，但 IgA 肾小球肾炎、膜性肾小球肾炎、狼疮性肾炎和常染色体显性多囊肾病也是 CKD 的常见病因。
临床指南推荐使用包含四个变量（血清肌酐、年龄、性别和种族）的估计肾小球滤过率（eGFR）来评估、分类和分层 CKD，这是 CKD 的常规生物标志物。然而，这些临床生物标志物存在一些局限性，特别是在 CKD 的早期阶段、老年人、极端体重指数值（血清肌酐）或受炎症、类固醇治疗和甲状腺功能障碍（血清胱抑素 C）影响时。因此，需要确定其他非侵入性生物标志物，这些标志物在临床上有助于改善 CKD 的诊断、预后和指导治疗管理。
CKD 是一种多因素疾病，与遗传和环境危险因素有关。因此，许多研究采用了遗传、表观遗传和转录组学方法来鉴定肾脏疾病的生物标志物。在这篇综述中，我们总结了过去十年中在人类中研究 CKD 基因组生物标志物的最重要的研究。包括 UMOD、SHROOM3 和 ELMO1 在内的几个基因与肾脏疾病密切相关，其某些特征，如 eGFR 和血清肌酐。在 CKD 和相关疾病中，表观遗传和转录组学生物标志物的作用仍不清楚。将多种生物标志物（包括基因组和/或表观基因组）组合到分类器中，可能会更全面地了解肾脏疾病。
