{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:31:07Z","timestamp":1786681867670,"version":"build-2736575974"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"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. Nutr."],"abstract":"\n Objective<\/jats:title>\n To examine associations between objectively quantified tongue features and the Controlling Nutritional Status (CONUT) score in patients with diabetic kidney disease (DKD), and to assess the influence of renal function and glycaemic status on these associations.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n This dual-centre cross-sectional study included 392 patients with DKD. Fifty-one tongue features were extracted using the YZAI-02 AI tongue imaging system. CONUT was calculated from serum albumin, lymphocyte count, and total cholesterol after multiple imputation by chained equations (\n m<\/jats:italic>\n \u202f=\u202f20). Rubin-pooled partial Spearman correlations, adjusted single-feature and joint multivariable linear regression, sensitivity analyses, and ordinal logistic regression were performed. Covariates were selected a priori and included age, sex, study centre, estimated glomerular filtration rate (eGFR), and HbA1c. Exploratory stratified and interaction analyses were conducted according to eGFR and sex.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The median CONUT score was 3 (interquartile range: 2\u20135), and 304 patients (77.6%) had CONUT-defined malnutrition. Five of 51 tongue features reached nominal significance in partial-correlation screening, although none survived false discovery rate correction across all 51 comparisons. After collinearity pruning, five representative features met the exploratory false discovery rate threshold in adjusted single-feature models. In the joint multivariable model, higher edge tongue saturation (\n \u03b2<\/jats:italic>\n \u202f=\u202f0.269 per standard deviation, 95% CI: \u22120.023 to 0.560,\n p<\/jats:italic>\n \u202f=\u202f0.071) and lower middle tongue brightness (\u03b2\u202f=\u202f\u22120.234, 95% CI: \u22120.495 to 0.026,\n p<\/jats:italic>\n \u202f=\u202f0.078) showed borderline associations with higher CONUT scores. Adding eGFR increased the coefficient for edge tongue saturation from 0.131 to 0.289, consistent with statistical suppression, whereas the coefficient for middle tongue brightness remained stable. Additional proteinuria adjustment produced coefficient changes below 15%, and both estimates remained imprecise. In ordinal logistic regression, edge tongue saturation showed a directionally consistent borderline association (OR\u202f=\u202f1.29, 95% CI: 0.96\u20131.71,\n p<\/jats:italic>\n \u202f=\u202f0.086). No interaction remained significant after false discovery rate correction.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n CONUT-defined malnutrition was common in DKD. Middle tongue brightness and edge tongue saturation showed modest, borderline associations with nutritional risk, suggesting that AI-assisted tongue phenotyping warrants further evaluation as a complement to nutritional surveillance in DKD.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fnut.2026.1902694","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:23:46Z","timestamp":1786681426000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Objective tongue phenotyping and nutritional risk in diabetic kidney disease: a dual-centre study linking quantified tongue features to the controlling nutritional status score"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhaoxi","family":"Dong","sequence":"first","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayou","family":"Liu","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Hospital of Integrated Traditional Chinese and Western Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Su","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengyi","family":"Cai","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihong","family":"Chen","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengdong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology","place":["Hefei, Anhui, China"]},{"name":"Artificial Intelligence Laboratory, Hefei Yunzhen Information Technology Co., Ltd.","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Shi","sequence":"additional","affiliation":[{"name":"Treatment Center of Kidney Disease, The First Affiliated Hospital of Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/s0140-6736(20)30045-3","article-title":"Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the global burden of disease study 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