{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:05:20Z","timestamp":1784696720934,"version":"3.55.0"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"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>The rising prevalence of early-onset type 2 diabetes (T2DM) has become a major public health concern, with these patients facing a particularly high risk of early diabetic kidney disease (DKD). Although continuous glucose monitoring (CGM)-derived metrics have shown promise in predicting complications in later-onset T2DM, their utility in early-onset T2DM, a more aggressive phenotype characterized by rapid \u03b2-cell decline and heightened complication risk, remains unclear. Moreover, the relationship between glycemic variability and renal injury, as well as the mechanisms underlying the associations between CGM metrics and DKD, have not been fully elucidated. This study aimed to evaluate the associations of time in range (TIR), coefficient of variation (CV), and glycemic risk index (GRI) with early DKD in Chinese patients with early-onset T2DM, and to explore the potential mediating role of triglycerides.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional study included 810 individuals with early-onset T2DM, defined as diagnosis before age 40. All participants underwent \u22657 days of CGM. Early DKD was defined as a urinary albumin-to-creatinine ratio (UACR) \u226530 mg\/g with an estimated glomerular filtration rate (eGFR) \u226560 mL\/min\/1.73m\u00b2. Multivariable logistic regression, restricted cubic splines, and mediation analysis were used to evaluate independent associations, nonlinear relationships, and the mediating role of triglycerides (TG), respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the participants, 183 (22.59%) had early DKD. After full adjustment including HbA1c, higher TIR was associated with lower odds of early DKD (per 1% increase: OR 0.99, 95% CI 0.98\u20130.99,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.001), whereas higher GRI was associated with increased odds (per 1-unit increase: OR 1.01, 95% CI 1.01\u20131.02,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.001). A U-shaped relationship was observed between CV and early DKD risk, with the nadir observed at approximately 20\u201330% CV. In mediation analysis, TG statistically accounted for 13.01% of the association between TIR and early DKD, and 11.92% of the association between GRI and early DKD.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In Chinese patients with early-onset T2DM, CGM-derived metrics are independently associated with early DKD, with TIR and GRI showing opposite associations and CV exhibiting a U-shaped relationship. The observed association involving triglycerides suggests that lipid metabolism may be a correlate of glycemic control in relation to renal injury. These findings support a multidimensional strategy integrating glucose control, glycemic stability, and lipid management for renal protection in this high-risk population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1884596","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:40:59Z","timestamp":1784695259000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Association of continuous glucose monitoring metrics with early diabetic kidney disease in early-onset type 2 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Yuanshuang","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Second Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Second Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Second Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-024-03772-w","article-title":"Early onset type 2 diabetes mellitus: an update","volume":"85","author":"Strati","year":"2024","journal-title":"Endocrine"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m997","article-title":"Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study","volume":"369","author":"Li","year":"2020","journal-title":"BMJ"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1466428","article-title":"Comparison of incidence trends of early-onset and late-onset type 2 diabetes in the Asia-Pacific region, 1990-2021: a join point regression analysis based on the global burden of disease study 2021","volume":"16","author":"Lin","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1093\/pcmedi\/pbac015","article-title":"Health consequences of early-onset compared with late-onset type 2 diabetes mellitus","volume":"5","author":"Hou","year":"2022","journal-title":"Precis Clin Med"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-022-00645-1","article-title":"Youth-onset type 2 diabetes mellitus: an urgent challenge","volume":"19","author":"Bjornstad","year":"2023","journal-title":"Nat Rev Nephrol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"3381","DOI":"10.1186\/s12889-025-24674-0","article-title":"Epidemiological trend in diabetic kidney disease (DKD) burden in China from 1990 to 2021, and projections for 2041: an analysis of the global burden of disease study 2021","volume":"25","author":"Li","year":"2025","journal-title":"BMC Public Health"},{"key":"B7","doi-asserted-by":"publisher","first-page":"752","DOI":"10.3390\/biom13050752","article-title":"Update on diabetic kidney disease (DKD): Focus on non-albuminuric DKD and cardiovascular risk","volume":"13","author":"Scilletta","year":"2023","journal-title":"Biomolecules"},{"key":"B8","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1111\/dom.14638","article-title":"Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin","volume":"24","author":"Gomez-Peralta","year":"2022","journal-title":"Diabetes Obes Metab"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2017.0035","article-title":"Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes","volume":"19","author":"Rodbard","year":"2017","journal-title":"Diabetes Technol Ther"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-019-05027-0","article-title":"Positioning time in range in diabetes management","volume":"63","author":"Advani","year":"2020","journal-title":"Diabetologia"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.2337\/dc18-1444","article-title":"Validation of time in range as an outcome measure for diabetes clinical trials","volume":"42","author":"Beck","year":"2019","journal-title":"Diabetes Care"},{"key":"B12","doi-asserted-by":"publisher","first-page":"S97","DOI":"10.2337\/dc23-S006","article-title":"6. 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