{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:00:56Z","timestamp":1784761256245,"version":"3.55.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Current screening for diabetic kidney disease (DKD) relies on the estimated glomerular filtration rate (eGFR) and albuminuria, which often fail to detect early tubular dysfunction and non-albuminuric phenotypes. The integration of macroscopic urine physical characteristics with metabolic signatures may offer a novel approach to precision stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a multicenter, prospective-retrospective cohort study involving 364 participants with type 2 diabetes. We developed \u201cFluxPro-DKD fusion model,\u201d that integrates \u201cDigital Physicalomics\u201d (computer-vision quantification of urine foam stability and chromaticity) and \u201cDual-Fluid Metabolomics\u201d (serum-to-urine flux ratios). The model was trained in a discovery cohort (\n                      <jats:italic>n<\/jats:italic>\n                      =282) and tested in an independent external validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      =82). The primary outcome was the detection of early-stage DKD. We also assessed the model\u2019s prognostic utility for major adverse renal events over a simulated 3-year period.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Metabolic profiling identified a distinct \u201cserum-to-urine flux mismatch\u201d of protein-bound uremic toxins (e.g., indoxyl sulfate), suggesting tubular secretory failure prior to glomerular damage. Digital physicalomics revealed that urine foam half-life was correlated with albuminuria (\n                      <jats:italic>r<\/jats:italic>\n                      =0.78). In the discovery cohort, the FluxPro-DKD fusion model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 (95% confidence interval [CI], 0.87 to 0.93), significantly outperforming the standard clinical model (AUC, 0.78;\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt;0.001). The model maintained robust discrimination in the external validation cohort (AUC, 0.85; 95% CI, 0.79 to 0.91). Among patients with normoalbuminuria, those classified as high-risk by the model had a significantly higher projected 3-year event rate than those classified as low-risk (35.3% vs. 2.3%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The integration of digital urine physical phenotypes and metabolic flux ratios effectively reveals early tubular secretory dysfunction and improved risk stratification for diabetic kidney disease compared with standard clinical metrics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1802447","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:33:54Z","timestamp":1776832434000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Integration of digital physicalomics and dual-fluid metabolomics flux ratios reveals tubular secretory dysfunction in early diabetic kidney disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Haili","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Traditional Chinese Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]},{"name":"Institute of Ethnic Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Dongfang Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Third Affiliated Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Third Affiliated Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Third Affiliated Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]},{"name":"Institute of Ethnic Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqiao","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]},{"name":"Institute of Ethnic Medicine, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mianzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Tianjin Hospital of Integrated Traditional Chinese and Western Medicine (ITCWM) Nankai Hospital","place":["Tianjin, China"]},{"name":"Nephrology Center, Dongfang Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]},{"name":"Department of Nephrology, Tianjin Institute of Nephropathy of Traditional Chinese Medicine","place":["Tianjin, 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