{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T02:07:08Z","timestamp":1793239628241,"version":"4.5.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetic kidney disease (DKD) poses a significant health burden with inadequate diagnostic sensitivity. This study develops non-invasive biomarkers by integrating urinary and renal single-cell sequencing with machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed DKD single-cell and bulk transcriptomic data from public repositories. We established a computational pipeline to distinguish kidney-originating cells in urinary sediments, enabling the identification of injury-associated gene signatures. These signatures were refined using machine learning to develop a diagnostic model, which was validated in independent cohorts. The biomarkers were further verified in DKD renal tissues at single-cell resolution and across multiple nephropathies. Functional and spatial analyses confirmed biological relevance using transcriptomic and histological validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Single-cell analysis of 2,089 urine-derived cells identified eight renal cell types, including injured proximal tubule cells (Inj-PTC) showing upregulated injury markers (HAVCR1, VCAM1) and enriched apoptotic\/TGF-\u03b2 pathways. A machine learning-selected biomarker panel (PDK4, RHCG, FBP1) demonstrated strong diagnostic value (area under the curve, AUC\u2009&gt;\u20090.9), with consistent downregulation across multiple chronic kidney diseases. PDK4 and FBP1 were specifically suppressed in DKD renal Inj-PTC (p\u2009&lt;\u20090.05). Functional analyses revealed their involvement in glucose metabolic pathways, and their cell type-specific expression patterns were confirmed by transcriptomic and immunohistochemical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study identifies a three-gene biomarker panel (PDK4, RHCG, FBP1) as a promising non-invasive diagnostic tool for DKD. While demonstrating excellent diagnostic performance. It represents a tubular injury-associated gene signature that is detectable in urinary cells and shows strong association with DKD in transcriptomic datasets, presenting a promising candidate for a non-invasive diagnostic assay.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0340096","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T18:46:02Z","timestamp":1767379562000},"page":"e0340096","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["A non-invasive urinary diagnostic signature for diabetic kidney disease revealed by machine learning and single-cell 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damage to the renal proximal tubules in children with idiopathic nephrotic syndrome","volume":"46","author":"A Kepka","year":"2008","journal-title":"Clin Chem Lab Med"},{"issue":"4","key":"pone.0340096.ref036","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1681\/ASN.2021050680","article-title":"Decreased renal gluconeogenesis is a hallmark of chronic kidney disease","volume":"33","author":"T Verissimo","year":"2022","journal-title":"J Am Soc Nephrol"},{"issue":"3","key":"pone.0340096.ref037","doi-asserted-by":"crossref","first-page":"181","DOI":"10.4093\/dmj.2014.38.3.181","article-title":"The role of pyruvate dehydrogenase kinase in diabetes and obesity","volume":"38","author":"I-K Lee","year":"2014","journal-title":"Diabetes Metab J"},{"key":"pone.0340096.ref038","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.redox.2018.06.003","article-title":"Adropin regulates pyruvate dehydrogenase in cardiac cells via a novel GPCR-MAPK-PDK4 signaling 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Huang","year":"2025","journal-title":"J Am Soc Nephrol"},{"issue":"4","key":"pone.0340096.ref042","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.kint.2016.10.011","article-title":"Pyruvate dehydrogenase kinase 4 deficiency attenuates cisplatin-induced acute kidney injury","volume":"91","author":"CJ Oh","year":"2017","journal-title":"Kidney Int"},{"issue":"4","key":"pone.0340096.ref043","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.kint.2023.06.022","article-title":"Inhibition of pyruvate dehydrogenase kinase 4 ameliorates kidney ischemia-reperfusion injury by reducing succinate accumulation during ischemia and preserving mitochondrial function during reperfusion","volume":"104","author":"CJ Oh","year":"2023","journal-title":"Kidney Int"},{"key":"pone.0340096.ref044","doi-asserted-by":"crossref","first-page":"362562","DOI":"10.1155\/2015\/362562","article-title":"Human urine as a noninvasive source of kidney cells","volume":"2015","author":"F 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