Metabolic Profiles of Type 2 Diabetes and Their Association With Renal Complications

被引:3
|
作者
Li, Shen [1 ]
Cui, Mengxuan [2 ]
Liu, Yingshu [3 ]
Liu, Xuhan [3 ]
Luo, Lan [3 ]
Zhao, Wei [3 ]
Gu, Xiaolan [3 ]
Li, Linfeng [2 ]
Liu, Chao [2 ]
Bai, Lan [2 ]
Li, Di [4 ]
Liu, Bo [5 ]
Che, Defei [6 ]
Li, Xinyu [3 ]
Wang, Yao [2 ,8 ]
Gao, Zhengnan [3 ,7 ]
机构
[1] Cent Hosp Dalian Univ Technol, Dept Cent Lab, Dalian 116000, Peoples R China
[2] Yidu Cloud Technol Inc, Beijing 100101, Peoples R China
[3] Cent Hosp Dalian Univ Technol, Dept Endocrinol, Dalian 116000, Peoples R China
[4] Cent Hosp Dalian Univ Technol, Dept Neurointervent, Dalian 116000, Peoples R China
[5] Dalian Univ Technol, Sch Biomed Engn, Dalian 116024, Peoples R China
[6] Cent Hosp Dalian Univ Technol, Dept Med Equipment, Dalian 116000, Peoples R China
[7] Cent Hosp Dalian Univ Technol, Dept Endocrinol, Xuegong St,42 Shahekou Dist,Xinan Rd, Dalian 116000, Liaoning, Peoples R China
[8] Yidu Cloud Technol Inc, Huayuan North St 35, Beijing 100101, Peoples R China
来源
关键词
diabetic kidney disease; eGFR decline; end-stage renal disease; metabolic syndrome; type; 2; diabetes; CHRONIC KIDNEY-DISEASE; RISK; MICROALBUMINURIA; PREVALENCE; SUBGROUPS; CHINESE;
D O I
10.1210/clinem/dgad643
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Context: The components of metabolic syndrome (MetS) are interrelated and associated with renal complications in patients with type 2 diabetes (T2D). Objective: We aimed to reveal prevalent metabolic profiles in patients with T2D and identify which metabolic profiles were risk markers for renal progression. Methods: A total of 3556 participants with T2D from a hospital (derivation cohort) and 931 participants with T2D from a community survey (external validation cohort) were included. The primary outcome was the onset of diabetic kidney disease (DKD), and secondary outcomes included estimated glomerular filtration rate (eGFR) decline, macroalbuminuria, and end-stage renal disease (ESRD). In the derivation cohort, clusters were identified using the 5 components of MetS, and their relationships with the outcomes were assessed. To validate the findings, participants in the validation cohort were assigned to clusters. Multivariate odds ratios (ORs) of the primary outcome were evaluated in both cohorts, adjusted for multiple covariates at baseline. Results: In the derivation cohort, 6 clusters were identified as metabolic profiles. Compared with cluster 1, cluster 3 (severe hyperglycemia) had increased risks of DKD (hazard ratio [HR] [95% CI]: 1.72 [1.39-2.12]), macroalbuminuria (2.74 [1.84-4.08]), ESRD (4.31 [1.16-15.99]), and eGFR decline [P < .001]; cluster 4 (moderate dyslipidemia) had increased risks of DKD (1.97 [1.53-2.54]) and macroalbuminuria (2.62 [1.61-4.25]). In the validation cohort, clusters 3 and 4 were replicated to have significantly increased risks of DKD (adjusted ORs: 1.24 [1.07-1.44] and 1.39 [1.03-1.87]). Conclusion: We identified 6 prevalent metabolic profiles in patients with T2D. Severe hyperglycemia and moderate dyslipidemia were validated as significant risk markers for DKD.
引用
收藏
页码:1051 / 1059
页数:9
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