Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype-phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD.

中文翻译：

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综合多组学方法改善慢性肾脏病的分类。

慢性肾脏病（CKD）目前根据其临床特征、相关合并症和活检损伤模式进行分类。即使在给定的分类中，疾病的表现、进展和治疗反应也存在相当大的差异，突出了潜在生物学机制的异质性。因此，患者和临床医生在考虑最佳治疗方法和风险预测时会遇到不确定性。现在，技术进步使得能够从 CKD 基因型-表型连续体的个体和患者群体中获取大规模数据集，包括 DNA 和 RNA 序列数据、蛋白质组学和代谢组学数据。使用机器学习等计算方法组合这些高维数据集（其中变量的数量超过临床结果观察的数量）的能力提供了将患者重新分类为分子定义的亚组的机会，从而更好地反映潜在的疾病机制。CKD 患者特别适合从这些综合性多组学方法中受益，因为用于生成这些不同类型分子数据的肾活检、血液和尿液样本是在常规临床护理过程中经常获得的。开发综合分子分类的最终目标是改进诊断分类、风险分层和分子、疾病特异性治疗的分配，以改善 CKD 患者的护理。