{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:59:25Z","timestamp":1791518365394,"version":"4.3.4"},"reference-count":136,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"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>Diabetic kidney disease (DKD) is a major microvascular complication of diabetes, often progressing silently and leading to end-stage kidney disease (ESKD) and cardiovascular morbidity. Early identification and risk-adapted intervention are crucial to improving long-term outcomes, yet existing clinical workflows are limited by delayed diagnosis and underutilization of available therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose and evaluate a multimodal, risk-driven framework for the early recognition and individualized management of DKD. The approach integrates: (1) standard renal function metrics\u2014estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (uACR)\u2014together with validated prediction models; (2) molecular biomarkers including metabolomics, gut microbiota, and peritoneal dialysis effluent signatures; (3) digital phenotypes derived from standardized acquisition of tongue images and pulse waveforms, rooted in Traditional Chinese Medicine (TCM) diagnostics; and (4) longitudinal data from wearable devices and remote monitoring platforms. Digital features are quantified using image processing and optical signal analysis and incorporated into multimodal prediction models. Treatment is escalated based on risk stratification using renin\u2013angiotensin\u2013aldosterone system (RAAS) inhibitors, sodium\u2013glucose cotransporter 2 (SGLT2) inhibitors, non-steroidal mineralocorticoid receptor antagonists (MRAs), and glucagon-like peptide-1 (GLP-1) receptor agonists. Real-time monitoring of therapeutic efficacy and safety is conducted using process end points such as eGFR slope and uACR trends.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Incorporation of quantifiable tongue and pulse features provides a novel, low-cost, and non-invasive risk enrichment layer that complements biochemical and omics-based markers. Multilayered risk stratification enables earlier identification of fast progressors and more timely treatment intensification. Evidence from landmark trials\u2014including Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD), Empagliflozin in Patients with Chronic Kidney Disease (EMPA-KIDNEY), Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease (FIDELIO-DKD), and Effects of Semaglutide on Chronic Kidney Disease (FLOW)\u2014supports the clinical utility of this approach. A closed-loop monitoring strategy based on process metrics and safety thresholds is proposed. We also outline ethical, regulatory, and data governance considerations necessary for clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of traditional clinical markers, digital TCM-derived phenotypes, and multi-omics data represents a promising paradigm for early, personalized, and dynamic DKD care. Future research should focus on external validation, impact on hard end points, and equitable deployment across real-world settings. This approach may help close the current diagnostic and therapeutic gaps in DKD management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1728293","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T06:39:59Z","timestamp":1767940799000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Emerging technologies for early risk stratification and precision management of diabetic kidney disease: a multimodal framework integrating digital phenotypes and clinical biomarkers"],"prefix":"10.3389","volume":"16","author":[{"given":"Lingdong","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lankun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anning","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijia","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF 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