{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T03:06:20Z","timestamp":1791947180630,"version":"4.3.4"},"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosmedicine.org"],"crossmark-restriction":false},"short-container-title":["PLoS Med"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic kidney disease (CKD) occurs in 20\u201350% of the people living with Type 2 diabetes (T2D) and is the leading cause of kidney failure worldwide. The cause of CKD is not fully understood, and few interventions prevent CKD in individuals living with diabetes. Here, we use large-scale proteomics data to identify circulating proteins that mediate the relationship between T2D and kidney disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>\n                      First, we used two-sample mendelian randomization (MR) and identified 71 circulating proteins whose levels were altered by genetic predisposition to T2D based on circulating proteomic GWAS from deCODE with 35,559 individuals and T2D GWAS with 80,154 cases. Then, we used\n                      <jats:italic>cis<\/jats:italic>\n                      -genetic variants to proxy the causal effect of some of these T2D-influenced circulating proteins and found that, collectively, five proteins (INHBC, GNPTG, LPO, AGRN, and CTSD) affected three kidney traits (blood urea nitrogen [BUN], estimated glomerular filtration rate [eGFR] and CKD risk) based on GWAS with up to 1,004,040 participants. Notably, we found that higher levels of circulating INHBC protein were estimated to lead to a lower eGFR and higher BUN based on MR analyses. We then replicated this MR analysis with proteomic GWAS from four additional cohorts, namely, UKB-PPP, Fenland, ARIC, and EPIC-Norfolk. We observed a consistent direction of effect across all four proteomic GWAS datasets, supporting the robustness of our results against platform and cohort variation. In observational analyses, increased circulating INHBC levels were associated with increased hazard for kidney disease diagnosis in 37,854 UK Biobank participants. We estimated that circulating INHBC levels mediate 1.3% (95% confidence interval [0.85%, 1.9%]) of the association between T2D and kidney disease diagnosis. There are important limitations in this study. Firstly, although we observed limited evidence for violations to the MR assumptions, some are untestable. Secondly, our study was not based on individuals with diabetic kidney diseases, but rather independent population-based studies assessing diabetes and kidney function separately. Therefore, additional functional analyses in disease specific cohort are needed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Collectively, these findings suggest that T2D influences the risk of CKD, in part, through increased circulating INHBC levels.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pmed.1004802","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T17:45:43Z","timestamp":1789148743000},"page":"e1004802","update-policy":"https:\/\/doi.org\/10.1371\/journal.pmed.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study"],"prefix":"10.1371","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3268-0058","authenticated-orcid":true,"given":"Kevin Y. 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Brent","family":"Richards","sequence":"additional","affiliation":[{"name":"Jewish General Hospital","place":["Montr\u00e9al, Qu\u00e9bec, Canada"],"department":["Lady Davis Institute for Medical Research"]},{"name":"5 Prime Sciences Inc","place":["Montr\u00e9al, Qu\u00e9bec, Canada"]},{"name":"McGill University","place":["Montr\u00e9al, Qu\u00e9bec, Canada"],"department":["Department of Human Genetics"]},{"name":"McGill University","place":["Montr\u00e9al, Qu\u00e9bec, Canada"],"department":["Department of Epidemiology, Biostatistics and Occupational Health"]},{"name":"King\u2019s College London","place":["London, United Kingdom"],"department":["Department of Twin Research"]},{"name":"McGill University","place":["Montr\u00e9al, Quebec, Canada"],"department":["Department of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"pmed.1004802.ref001","article-title":"Pathogenetic mechanisms of 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