BMC endocrine disorders · 2026
Novel phenotypic clusters of adult-onset diabetic kidney disease based on static and dynamic clustering algorithms - a data-driven multi-center study.
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Li Kun, Wei Lingling, Yin Ruili, Xu Miao, Zhao Qiubo, Ke Jing, Yang Longyan
Аннотация
OBJECTIVE: This study aimed to identify clinically interpretable DKD sub-phenotypes for personalized prognosis and intervention.
METHODS: We enrolled participants with type 2 diabetes mellitus (T2DM) from three hospitals in China. Clinically interpretable sub-phenotypes were identified using two complementary clustering frameworks: a static model (k-means clustering) capturing the metabolic-renal profile at DKD diagnosis, and a dynamic model (group-based multi-trajectory model, GBMTM) reconstructing longitudinal trajectories preceding diagnosis. Associations between DKD clusters and post-diagnosis DKD progression were estimated using cox regression.
RESULTS: A total of 1520 participants were included for the analysis of static clustering, 32.53% were females with a mean age of 54.4 ± 12.1 years. The static clustering method classified DKD patients into four clusters: BALANCE, YOUTH, TYPICAL and NATURAL. Compared with the participants in YOUTH, the hazard ratios (HRs) of progressing to higher stage of CKD (G3b-G5) in BALANCE, TYPICAL and NATURAL are 1.68 (95%CI:0.81-3.49), 7.85 (95%CI:4.25-14.51), 6.58 (95%CI:3.54-12.24), respectively. GBMTM identified three longitudinal trajectories. The HRs (95%CI) of progressing to higher stage of CKD (G3b-G5) was 2.48 (95%CI:1.50-4.09) in Trajectory 1 compared with Trajectory 2. In addition, the clustering results obtained from the validation cohort were consistent with the derivation cohort.
CONCLUSIONS: By integrating static and dynamic clustering approaches, this study identifies novel DKD sub-phenotypes, and evaluated the risk of DKD progression in each cluster.
METHODS: We enrolled participants with type 2 diabetes mellitus (T2DM) from three hospitals in China. Clinically interpretable sub-phenotypes were identified using two complementary clustering frameworks: a static model (k-means clustering) capturing the metabolic-renal profile at DKD diagnosis, and a dynamic model (group-based multi-trajectory model, GBMTM) reconstructing longitudinal trajectories preceding diagnosis. Associations between DKD clusters and post-diagnosis DKD progression were estimated using cox regression.
RESULTS: A total of 1520 participants were included for the analysis of static clustering, 32.53% were females with a mean age of 54.4 ± 12.1 years. The static clustering method classified DKD patients into four clusters: BALANCE, YOUTH, TYPICAL and NATURAL. Compared with the participants in YOUTH, the hazard ratios (HRs) of progressing to higher stage of CKD (G3b-G5) in BALANCE, TYPICAL and NATURAL are 1.68 (95%CI:0.81-3.49), 7.85 (95%CI:4.25-14.51), 6.58 (95%CI:3.54-12.24), respectively. GBMTM identified three longitudinal trajectories. The HRs (95%CI) of progressing to higher stage of CKD (G3b-G5) was 2.48 (95%CI:1.50-4.09) in Trajectory 1 compared with Trajectory 2. In addition, the clustering results obtained from the validation cohort were consistent with the derivation cohort.
CONCLUSIONS: By integrating static and dynamic clustering approaches, this study identifies novel DKD sub-phenotypes, and evaluated the risk of DKD progression in each cluster.