{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:58:27Z","timestamp":1790776707504,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Since the first successful kidney transplant in 1954, transplant medicine has advanced significantly, achieving favorable one-year outcomes. However, despite excellent short-term outcomes, long-term renal allograft survival remains sub-optimal, with acute rejection continuing to be a major contributor to death-censored allograft loss. Current approaches for post-transplant surveillance and rejection monitoring rely on either nonspecific markers that often detect injury late in their course or invasive procedures associated with procedural risks and complications. To address these limitations, numerous non-invasive biomarkers have been introduced into solid organ transplantation research with the goal of improving allograft surveillance, facilitating earlier detection of rejection and complementing existing diagnostic approaches. Although several biomarkers have demonstrated clinical promise, none currently possess sufficient accuracy to replace kidney allograft biopsy. Biomarkers under investigation include blood-, tissue- and urine-based specimens. Their development and validation as independent markers have been limited by smaller sample size, heterogenous targets, variable performance characteristics and suboptimal sensitivity and specificity. In response, the field has increasingly shifted toward multimodal approaches that combine biomarkers and incorporate artificial intelligence to improve diagnostic accuracy and outcomes prediction. This review summarizes current biomarkers, discusses their limitations and explores future directions for biomarker development and clinical implementation.<\/jats:p>","DOI":"10.3390\/jcm15197601","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:01:19Z","timestamp":1790773279000},"page":"7601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Biomarker Revolution in Kidney Transplantation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9077-4304","authenticated-orcid":false,"given":"Elizabeth","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Medicine, Division of Nephrology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yvette Y.","family":"Lopez","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7154-0889","authenticated-orcid":false,"given":"Sami","family":"Alasfar","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"A8","DOI":"10.1053\/j.ajkd.2022.12.001","article-title":"US Renal Data System 2022 Annual Data Report: Epidemiology of Kidney Disease in the United States","volume":"81","author":"Johansen","year":"2023","journal-title":"Am. 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