The research team, led by Kevin Y. H. Liang, Thomas M. Zheng, Dandan Tan and colleagues, including senior author J. Brent Richards, employed a technique known as two-sample mendelian randomization. This method exploits the random allocation of genetic variants at conception, which act much like the assignment groups in a randomized clinical trial. Because genetic variants inherited from parents are fixed at birth and are not confounded by later lifestyle factors or reverse causation, they can be used to test whether an exposure, such as a genetic predisposition to Type 2 diabetes, genuinely causes a change in an outcome, such as kidney function. The approach is particularly valuable in fields like diabetes research, where observational associations are notoriously vulnerable to confounding.

In the first stage of the study, the investigators asked a deceptively simple question: which of the thousands of proteins circulating in human blood are changed by a genetic predisposition to Type 2 diabetes? To answer it, they drew on a proteomic genome-wide association study from the Icelandic biobank deCODE, which measured blood protein levels in 35,559 individuals, and combined it with a diabetes genome-wide association study encompassing 80,154 cases of Type 2 diabetes. Running mendelian randomization across the proteome, they identified 71 circulating proteins whose blood levels appear to be shifted by genetic liability to the disease. These proteins, in other words, form a candidate pool of molecular intermediaries that could plausibly carry the damaging signal of diabetes onward to the kidneys.

Identifying proteins that diabetes influences was only half the task. The team next needed to determine which of these diabetes-responsive proteins actually affect kidney health. For this, they used a second application of mendelian randomization, this time relying on cis-acting genetic variants, variants located near the gene encoding each protein that specifically regulate that protein’s circulating level. Such variants provide a cleaner proxy for the protein’s causal effect because they are less likely to influence the kidneys through unrelated pathways. Testing the candidate proteins against three kidney traits, blood urea nitrogen, estimated glomerular filtration rate, and diagnosed chronic kidney disease, using genetic data from up to 1,004,040 participants, the researchers converged on five proteins with credible causal roles: INHBC, GNPTG, LPO, AGRN and CTSD.

One protein stood out from the pack. INHBC, a circulating member of the inhibin-beta family with known ties to inflammatory and fibrotic signaling, showed a striking pattern: higher genetically influenced levels of the protein were estimated to lead to a lower estimated glomerular filtration rate, the standard measure of how well the kidneys filter blood, and to higher blood urea nitrogen, a marker of accumulating waste products that failing kidneys cannot clear. In practical terms, the analysis suggested that elevated INHBC pushes kidney function in the wrong direction on both fronts, making it the most compelling single mediator to emerge from the screen.

A crucial strength of the study lies in its replication strategy. Proteomic measurements are typically made on antibody-based platforms that can differ substantially between laboratories, and any single cohort may harbor idiosyncrasies that distort results. To guard against these artifacts, the team re-ran their mendelian randomization analysis for INHBC using proteomic genome-wide association data from four additional cohorts: the UK Biobank Pharma Proteomics Project, the Fenland study, the Atherosclerosis Risk in Communities study, and EPIC-Norfolk. Across all four independent datasets, the direction of effect was consistent, a result the authors interpret as evidence that their findings are robust to both platform differences and cohort variation. Such cross-cohort concordance is far from guaranteed in proteomic genetics and lends considerable weight to the central claim.

The genetic evidence was then complemented by a more traditional observational analysis. In 37,854 participants from the UK Biobank, individuals with higher measured circulating levels of INHBC faced an increased hazard of receiving a kidney disease diagnosis over follow-up. While observational associations of this kind cannot by themselves establish causation, their alignment with the genetic results creates a coherent picture: the same protein that genetic instruments implicate as a causal driver of reduced kidney function is also elevated in people who go on to develop kidney disease in the real world.

Perhaps the most consequential number in the study is a modest one. Using mediation analysis that combines the genetic estimates, the researchers calculated that circulating INHBC levels mediate approximately 1.3 percent of the association between Type 2 diabetes and kidney disease diagnosis, with a 95 percent confidence interval spanning 0.85 to 1.9 percent. On its face, 1.3 percent may sound underwhelming, and the authors are careful not to oversell it. Yet in a disease as widespread as diabetes, which affects hundreds of millions of people globally, even a small fractional contribution can translate into a substantial absolute burden of kidney disease. More importantly, the result establishes a proof of principle that specific, druggable-class molecules can be pinpointed as mediators of diabetic organ damage using human genetics alone, opening a template for discovering the remaining pathways that account for the other 98.7 percent.

The authors are equally candid about the limitations of their work. Although they observed limited evidence for violations of the mendelian randomization assumptions, some of those assumptions, such as the absence of pleiotropic pathways connecting the genetic instruments to kidney outcomes, are fundamentally untestable and can never be fully excluded. Furthermore, the study was not conducted in individuals with confirmed diabetic kidney disease; instead, it relied on independent population-based studies that assessed diabetes and kidney function separately. This distinction matters, because the biology of diabetic kidney disease may involve tissue-specific processes within the kidney itself that circulating protein levels only partially reflect. The authors emphasize that additional functional analyses in disease-specific cohorts will be needed before the findings can be translated into clinical practice.

Even with those caveats, the study arrives at a moment of genuine unmet need. Few interventions currently prevent chronic kidney disease in people living with diabetes beyond tight glycemic control, blood pressure management and newer agents such as SGLT2 inhibitors, and many patients progress to kidney failure despite the best available care. By nominating INHBC and four other proteins as causal candidates, the research provides a shortlist for laboratory follow-up, drug development and biomarker work. If future studies confirm that INHBC drives fibrotic or inflammatory injury in diabetic kidneys, therapies aimed at lowering its circulating levels could one day join the arsenal against diabetic kidney disease. For now, the study stands as a vivid demonstration of how human genetics, massive biobanks and proteomic technology can be woven together to illuminate the hidden molecular bridges between two of the world’s most common diseases.

Subject of Research: Protein mediators of chronic kidney disease in Type 2 diabetes identified through mendelian randomization

Article Title: Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study

Article References: Liang, K. Y. H., Zheng, T. M., Tan, D., Sasako, T., Ilboudo, Y., Chen, Y., Butler-Laporte, G., Yoshiji, S., & Richards, J. B. (2026). Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study. PLOS Medicine, 23(9), e1004802. https://doi.org/10.1371/journal.pmed.1004802

Image Credits: AI Generated

DOI: 10.1371/journal.pmed.1004802

Keywords: Type 2 diabetes, chronic kidney disease, mendelian randomization, proteomics, INHBC, glomerular filtration rate, UK Biobank, genetic epidemiology, kidney failure, biomarkers, PLOS Medicine, causal inference