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World J Diabetes. Jun 15, 2026; 17(6): 120343
Published online Jun 15, 2026. doi: 10.4239/wjd.120343
Published online Jun 15, 2026. doi: 10.4239/wjd.120343
Dynamic glycemic exposure and trajectory profiling for incident diabetic kidney disease: A longitudinal cohort study
Hui-Yu Zou, Xiao-Di Guo, Zhe-Yao Wen, Man-Man Wang, Fen Xu, Meng-Yin Cai, Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
Hui-Yu Zou, Xiao-Di Guo, Zhe-Yao Wen, Man-Man Wang, Fen Xu, Meng-Yin Cai, Guangdong Provincial Key Laboratory of Diabetology and Guangzhou Municipal Key Laboratory of Mechanistic and Translational Obesity Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
Hao Jiang, Department of Gastroenterology and Hepatology, Laboratory for Clinical Medicine, Beijing You'an Hospital, Capital Medical University, Beijing 100069, China
Yi Shi, Department of Endocrinology and Metabolism, The Fourth Hospital of Changsha, Changsha 410006, Hunan Province, China
An-Dong Zhou, Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
Co-first authors: Hui-Yu Zou and Hao Jiang.
Co-corresponding authors: Fen Xu and Meng-Yin Cai.
Author contributions: Zou HY and Jiang H contributed to the conception and design of the study, data collection, data analysis and interpretation, and manuscript drafting; Guo XD and Wen ZY assisted with data collection; Shi Y, Zhou AD and Wang MM contributed to the study’s conception and design; Xu F and Cai MY participated in manuscript revision and editing; Zou HY and Jiang H contributed equally to this manuscript and are co-first authors; Cai MY and Xu F contributed equally to this manuscript and are co-corresponding authors; all authors reviewed and approved the final version of the manuscript.
AI contribution statement: ChatGPT was used only to assist with English language polishing, grammar correction, and translation of author-prepared text. No AI tool was used to generate scientific content, perform data or statistical analyses, interpret results, create figures or tables, generate references, or participate in the study design. All authors have carefully reviewed and approved the final manuscript.
Supported by the National Natural Science Foundation of China, No. 82270942; the Natural Science Foundation of Guangdong Province, No. 2026A1515012884; and the Noncommunicable Chronic Diseases-National Science and Technology Major Project, No. 2024ZD0523200.
Institutional review board statement: The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, Guangdong Province, China), No. II2024-352-01.
Informed consent statement: Written informed consent was obtained from all participants prior to their involvement in the study.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: The dataset used during the current study is available from the corresponding author upon reasonable request.
Corresponding author: Meng-Yin Cai, PhD, Chief Physician, Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou 510630, Guangdong Province, China.
Received: February 24, 2026
Revised: March 13, 2026
Accepted: April 17, 2026
Published online: June 15, 2026
Processing time: 107 Days and 19.9 Hours
Revised: March 13, 2026
Accepted: April 17, 2026
Published online: June 15, 2026
Processing time: 107 Days and 19.9 Hours
Core Tip
Core Tip: This study demonstrates that dynamic glycemic tracking using time-weighted average glycated hemoglobin (HbA1c) significantly outperforms static baseline measurements in predicting incident diabetic kidney disease (DKD) in type 2 diabetes. We identified a critical non-linear risk inflection at 7.0% HbA1c, beyond which renal risk escalates precipitously. Furthermore, longitudinal trajectory profiling revealed that rapid early correction of severe hyperglycemia is associated with an attenuated risk of long-term renal deterioration. These findings highlight the prognostic superiority of quantifying cumulative glucotoxicity and emphasize that early, intensive glycemic intervention may be crucial to overcome therapeutic inertia and potentially mitigate the risk of DKD.