Key result
Urinary proteomic analysis combined with machine learning identified a 20-protein classifier that differentiated patients with hypertension-associated albuminuria from controls with an area under the curve of 0.98.
Why the study?
Hypertension is prevalent in Africa and independently increases the risk of kidney outcomes, but proteins and pathways involved in hypertension-associated albuminuria require identification.
Case-Control (n=73)
No
Urinary proteomic profiling combined with machine learning successfully differentiated black South African patients with hypertension-associated albuminuria from healthy controls, highlighting the involvement of immune and extracellular matrix pathways.
Urinary proteomics may identify pathways in hypertension-associated albuminuria; leaves open causal roles and generalizability.
Background: 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. Methods: 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 was 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. Results: 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]=1.4x10 -45 ), innate immune system (q=1.1x10 -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 CKD 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. Conclusions: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension and albuminuria.
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Govender et al. (2023) conducted a case-control in Hypertension-associated albuminuria (n=73). Hypertension and albuminuria vs. Normotensive, normoalbuminuric controls was evaluated on Differentially abundant urinary proteins. Urinary proteomic analysis combined with machine learning identified a 20-protein classifier that differentiated patients with hypertension-associated albuminuria from controls with an area under the curve of 0.98.
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