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New Study Identifies Key Biomarkers for CKD Treatment

A new study has unveiled 32 proteins linked to chronic kidney disease (CKD), including 20 novel biomarkers. This research paves the way for innovative treatments by integrating large-scale genomic, transcriptomic, and proteomic data to identify potential therapeutic targets.

Insights into Chronic Kidney Disease Research: Unveiling New Biomarkers and Therapeutic Targets

Chronic kidney disease (CKD) is a progressive condition that affects roughly 10% of the global population. Characterized by structural and functional damage to the kidneys, CKD increases the risk of severe outcomes such as kidney replacement therapy, cardiovascular incidents, and even death. Despite its widespread prevalence, the medical community has yet to find an effective cure for CKD, making the search for novel strategies to prolong kidney and patient survival crucial.

At a Glance

  • CKD Prevalence: Affects 10% of the world’s population.

  • Adverse Outcomes: Increased risk of kidney replacement therapy, cardiovascular events, and death.

  • Biomarker Identification: Essential for potential treatment targets.

  • Study Approach: Integrated top 3 largest GWAS for over 3000 proteins and CKD-related outcomes.

  • Key Findings: 32 proteins associated with CKD, including 20 novel proteins.

Revelations from a New Study

A new paper has recently shed light on the complex mechanisms behind CKD by identifying key biomarkers and potential therapeutic targets. This comprehensive study integrates data from the largest genome-wide association studies (GWAS) for more than 3,000 proteins, along with extensive GWAS of CKD-related outcomes. The research aims to deepen our understanding of CKD's biological pathways and identify novel biomarkers that could pave the way for new treatments.

Biological Mechanisms and Study Methods

In biological systems, information flows from DNA (genome) to RNA (transcriptome) to proteins (proteome). While large-scale GWAS have pinpointed numerous loci associated with CKD and kidney function, these loci are far from being viable therapeutic targets. The current blood proteome allows for high-throughput analysis and the identification of potential targets, including those enriched for CKD or its risk factors.

Mendelian Randomization (MR) is an approach that uses genetic variation as instrumental variables (IVs) to infer causal relationships between exposures and outcomes. This method can add evidence for causal inference in CKD proteomics research before moving on to dedicated animal models or randomized trials.

Key Findings

Identified Proteins and Their Associations

The study identified 32 proteins significantly associated with CKD, out of which 20 were novel findings. Here are some notable proteins and their associations:

  • MFAP4, IDI2, GATM, TCEA2: Negatively associated with CKD.

  • INHBC, LEAP2, AIF1, GCKR: Positively associated with CKD.

  • UMOD, sRAGE: Known markers, with sRAGE being significantly higher in CKD patients than in controls.

Impact on Kidney Function and CKD Subtypes

The study also explored associations of identified proteins with various CKD-related phenotypes, including kidney function (eGFRcrea and eGFRcys) and rapid kidney function decline. Notably, proteins like SDCCAG8, GATM, TCEA2, and FGF5 were negatively associated with rapid kidney function decline, while sRAGE, AIF1, and UMOD were positively associated.

Integration of Multi-Omics Data

The integration of genomics with transcriptome and proteome data is crucial for understanding CKD's underlying mechanisms. This study utilized large-scale GWAS datasets for plasma proteome involving significant sample sizes from Iceland, Fenland, and the UK Biobank. These datasets were matched with coding genes in available GWAS datasets for transcriptomes like eQTLGen and GTEx.

Comprehensive Analysis Pipeline

The study's comprehensive analysis pipeline included:

  1. Proteome-wide MR and Transcriptome-wide MR: To identify potential protein targets of CKD.

  2. Colocalization Analysis: To verify shared coding gene loci of identified proteins with CKD.

  3. Protein-Protein Interaction (PPI) and Gene Ontology (GO) Enrichment Analysis: To explore the biological mechanisms of putative protein targets.

Potential Therapeutic Targets

The study identified eight priority proteins for CKD treatment based on MR, SMR, and colocalization analysis:


These proteins and coding genes were mainly enriched in immunity-related pathways and showed specificity in kidney tissues or cells.


The identification of 32 CKD-related proteins, including 20 novel proteins, offers a promising pathway for developing new therapeutic targets for CKD. The study highlights the importance of integrating multi-omics data to deepen our understanding of CKD's biological mechanisms and potential treatment approaches.

In the words of the researchers:

A deeper identification and understanding of the biomarkers involved in the CKD biological pathways is essential for identifying potential treatment targets.
Si, S., Liu, H., Xu, L. et al. Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome. Genome Med 16, 84 (2024).

This comprehensive study sets the stage for future research to validate these findings and explore their clinical applications in treating CKD.