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

Author:

Li Shen1ORCID,Cui Mengxuan2ORCID,Liu Yingshu3ORCID,Liu Xuhan3,Luo Lan3,Zhao Wei3,Gu Xiaolan3,Li Linfeng2ORCID,Liu Chao2ORCID,Bai Lan2,Li Di4,Liu Bo5ORCID,Che Defei6,Li Xinyu3,Wang Yao2ORCID,Gao Zhengnan3ORCID

Affiliation:

1. Department of Central Laboratory, Central Hospital of Dalian University of Technology , Dalian 116000 , China

2. Yidu Cloud Technology Inc , Beijing 100101 , China

3. Department of Endocrinology, Central Hospital of Dalian University of Technology , Dalian 116000 , China

4. Department of Neurointervention, Central Hospital of Dalian University of Technology , Dalian 116000 , China

5. School of Biomedical Engineering, Dalian University of Technology , Dalian 116024 , China

6. Department of Medical Equipment, Central Hospital of Dalian University of Technology , Dalian 116000 , China

Abstract

Abstract 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.

Funder

National Key R&D Program of China

Dalian Science and Technology Bureau

Publisher

The Endocrine Society

Subject

Biochemistry (medical),Clinical Biochemistry,Endocrinology,Biochemistry,Endocrinology, Diabetes and Metabolism

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