Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10− 45), innate immune system (q = 1.1 × 10− 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76–100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

中文翻译：

---

高血压相关蛋白尿病理生理学的蛋白质组学见解：南非队列的初步研究

高血压是一个重要的公共卫生优先事项，在非洲发病率很高。它也是肾脏结局的独立危险因素。我们的目的是通过评估患有高血压和蛋白尿的南非黑人参与者与没有患有高血压和蛋白尿的参与者的尿液蛋白质组谱，来确定与高血压相关的蛋白尿有关的潜在蛋白质和途径。该研究纳入了 24 名同时患有高血压和蛋白尿的南非病例以及 49 名既不患有高血压又患有蛋白尿的对照参与者。从尿样中提取蛋白质，并使用超高效液相色谱结合质谱进行分析。使用数据独立采集 (DIA) 生成数据，并使用 Spectronaut™ 15 进行处理。在 Perseus 和 Cytoscape 上进行统计和功能数据注释，以识别和注释差异丰富的蛋白质。使用 OmicLearn 平台将机器学习应用于数据集。总体而言，对照组和病例组分别平均定量了 1,225 种和 915 种蛋白质。三百三十二个差异丰富的蛋白质被构建成一个网络。与这些差异丰富的蛋白质相关的途径包括免疫系统（q值[错误发现率] = 1.4 × 10− 45）、先天免疫系统（q = 1.1 × 10− 32）、细胞外基质（ECM）组织（q = 0.03）和基质金属蛋白酶的激活（q = 0.04）。高血压和慢性肾病疾病评分较高（76-100% 置信度）的蛋白质包括血管紧张素原 (AGT)、白蛋白 (ALB)、载脂蛋白 L1 (APOL1) 和尿调节素 (UMOD)。机器学习方法能够识别一组 20 种蛋白质，区分病例和对照。尿液蛋白质组数据与机器学习方法相结合，能够对疾病状态进行分类，并识别与高血压相关蛋白尿相关的蛋白质和途径。