Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease

Abstract

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

Keywords: Mendelian randomization; Proteomics; causal inference; chronic kidney disease; high-throughput; pQTL.

Graphical abstract

Fig. 1

Overview of proteomics in epidemiological studies on kidney diseases. Icon credit: Servier Medical Art by Servier (licensed under a Creative Commons Attribution 3.0 Unported License).

Fig. 2

The effect of adjustment for (measured) GFR in studies of proteomics data and ESKD. Here, we fitted Cox proportional hazards models for 7,285 proteins quantified in 703 participants of the AASK study. Protein abundance was log-transformed and adjusted for demographics included age, sex, and the randomization group. GFR, glomerular filtration rate; ESKD, end-stage kidney disease; AASK, the African American Study of Kidney Disease and Hypertension; ESKD, end-stage kidney disease.

Fig. 3

Conceptual parallels of Mendelian randomization (A) and a randomized controlled trial (B). Figure adapted from Figure 1 in Sanderson, et al. 2022. pQTL, protein Quantitative Trait Loci.