{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T13:59:36Z","timestamp":1767621576925,"version":"3.48.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"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>Metabolic syndrome is a key independent risk factor for the progression of chronic kidney disease (CKD) in patients with Type 2 diabetes (T2DM). Traditional studies often focus on isolated metabolic markers, but our research aims to comprehensively assess the metabolic landscape of these patients. Existing approaches have been limited in integrating multiple metabolic parameters and stratifying patients based on the severity of metabolic dysregulation, hindering the understanding of disease progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center, retrospective cohort study was conducted at Guangdong Provincial People\u2019s Hospital, enrolling 860 participants from January 2010 to December 2023. A total of 65.0% were male, and 35.0% were female. Using Latent Class Analysis (LCA), we stratified CKD patients with T2DM into two distinct classes based on a comprehensive set of baseline clinical metabolic indicators, including glycated hemoglobin (HbA1c), lipid profiles, serum uric acid, blood pressure and body mass index (BMI). Cox proportional hazards models were used to assess renal outcome risks across these identified metabolic phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LCA revealed that Class 2 exhibited significantly higher values for clinical parameters including systolic and diastolic blood pressure, BMI, total cholesterol, triglycerides, LDL-C, HbA1c, and protein-to-creatinine ratio. Longitudinal analysis showed increased hazard ratios for renal outcomes at 3, 5, and 10 years for Class 2 (HR: 1.718, 1.662, and 1.826, respectively; all P &amp;lt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study highlights the utility of comprehensive metabolic profiling through LCA for stratifying CKD patients with T2DM, identifying two distinct subgroups with differential renal prognoses, and offering insights for precision nephrology interventions and personalized risk management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1599024","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:04:17Z","timestamp":1767611057000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stratifying metabolic-related risk factors using latent class analysis to explore the risk of renal composite endpoints in patients with type 2 diabetes mellitus and associated chronic kidney disease"],"prefix":"10.3389","volume":"16","author":[{"given":"Xiaojie","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiting","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danfeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runli","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaxi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanchen","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qijun","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","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 2017","volume":"395","year":"2020","journal-title":"Lancet (London England)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2007.09.014","article-title":"Metabolic syndrome and the development of CKD in American Indians: the Strong Heart Study","volume":"51","author":"Lucove","year":"2008","journal-title":"Am J Kidney Dis"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.kisu.2017.10.003","article-title":"Diagnosis of diabetic kidney disease: state of the art and future perspective","volume":"8","author":"Persson","year":"2018","journal-title":"Kidney Int Suppl"},{"key":"B4","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.trsl.2016.12.004","article-title":"The metabolic syndrome and chronic kidney disease","volume":"183","author":"Zhang","year":"2017","journal-title":"Transl Res"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1097\/01.hjh.0000226206.03560.ac","article-title":"Waist circumference and the metabolic syndrome predict the development of elevated albuminuria in non-diabetic subjects: the DESIR Study","volume":"24","author":"Bonnet","year":"2006","journal-title":"J Hypertens"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1007\/s40200-023-01348-5","article-title":"Metabolic syndrome as a risk factor for the development of kidney dysfunction: a meta-analysis of observational cohort studies","volume":"23","author":"Valizadeh","year":"2024","journal-title":"J Diabetes Metab Disord"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S","article-title":"Definition, diagnosis and classification of diabetes mellitus and its complications. 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