{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:16:29Z","timestamp":1784297789994,"version":"3.55.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Nephrol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and hypothesis<\/jats:title>\n                    <jats:p>Diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) presents heterogeneously, complicating risk assessment. This study evaluated microRNAs and other biomarkers for DKD phenotyping and predicting kidney function in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 79 patients with T2DM were analyzed. DKD phenotypes were defined based on eGFR and UACR:<\/jats:p>\n                    <jats:p>\u2022 F1 (albuminuric): eGFR\u2009&lt;\u200960 mL\/min\/1.73\u00a0m\u00b2, UACR\u2009\u2265\u2009300\u00a0mg\/g.<\/jats:p>\n                    <jats:p>\u2022 F2 (non-albuminuric, preserved filtration): eGFR\u2009\u2265\u200960 mL\/min\/1.73\u00a0m\u00b2, UACR\u2009\u2264\u200930\u00a0mg\/g.<\/jats:p>\n                    <jats:p>\u2022 F3 (non-albuminuric, reduced filtration): eGFR\u2009&lt;\u200960 mL\/min\/1.73\u00a0m\u00b2, UACR\u2009\u2264\u200930\u00a0mg\/g.<\/jats:p>\n                    <jats:p>\u2022 F4 (moderately increased albuminuria): UACR\u2009&gt;\u200930 and &lt;\u2009300\u00a0mg\/g was shown for completeness but excluded from primary analyses.<\/jats:p>\n                    <jats:p>Multiple regression and partial least squares structural equation modeling (PLS-SEM) were applied to identify predictors of eGFR. Discriminant analysis (including age, ACE, uric acid, and AIP) was used for phenotype classification. Serum levels of hsa-miR-126-3p and hsa-miR-423-5p were compared between phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Independent predictors of eGFR included ACE (\u03b2 = \u2212\u20090.478;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), age (\u03b2 = \u2212\u20090.336), AIP (\u03b2 = \u2212\u20090.245), and hsa-miR-423-5p (\u03b2\u2009=\u20090.138;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.033) (R\u00b2 = 0.619). In the PLS-SEM model, ACE, AIP, and hsa-miR-423-5p had significant direct effects on eGFR, while ACE was modulated by hsa-miR-126-3p, age, and BMI. Discriminant analysis correctly classified 87.5% of patients (Wilks\u2019 Lambda,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). F1 exhibited the highest ACE and hsa-miR-126-3p levels, lowest HDL-C, and most microvascular complications. F2 had the best renal function, lowest ACE and miR-126-3p expression, and the highest proportion of women. F3 patients were the oldest, with elevated uric acid and hsa-miR-423-5p levels. Coronary heart disease was most common in F1 and F3, while stroke occurred only in F1 and F2.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ACE, AIP, and the miRNAs hsa-miR-126-3p and hsa-miR-423-5p may support DKD phenotyping and kidney function prediction. Incorporating these markers into clinical models could enable the implementation of individualized nephroprotective strategies in patients with T2DM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12882-025-04592-4","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:40:39Z","timestamp":1763545239000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The \u201cPicasso faces\u201d of diabetic kidney disease \u2013 how the art of phenotyping and molecular biomarkers is transforming clinical nephrology: an observational study in patients with type 2 diabetes"],"prefix":"10.1186","volume":"26","author":[{"given":"Ma\u0142gorzata","family":"Rodzo\u0144-Norwicz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Potocka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzena","family":"Skrzypa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izabela","family":"Zawlik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Milian\u2013Ciesielska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patryk","family":"Kogut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Gargasz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Maes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Sowa-Ku\u0107ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Gala-B\u0142\u0105dzi\u0144ska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"4592_CR1","doi-asserted-by":"publisher","first-page":"109119","DOI":"10.1016\/j.diabres.2021.109119","volume":"183","author":"H Sun","year":"2022","unstructured":"Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. 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