Fukai Chihaya, Chiba Shumpei, Itoga Takaaki, Kobayashi Gen, Kaku Kohei
EY Strategy and Consulting Co., Ltd, Tokyo, Japan.
Kawasaki Medical School, Okayama, Japan.
Diabetes Obes Metab. 2025 Jul;27(7):3686-3694. doi: 10.1111/dom.16390. Epub 2025 Apr 14.
AIMS/INTRODUCTION: While studies on kidney disease (KD) in patients with severe metabolic syndrome (MetS) have been reported, research on undiagnosed MetS individuals is limited. This study aimed to investigate KD mechanisms in early MetS stages among Japanese individuals to establish accurate KD prediction models applicable to specific health guidance using annual health check-up (HC) data.
Cox regression analysis was conducted using the Kokuho Database including HC and claims data over the past 10 years. Survival time was defined as the period from the initial HC during the observation period until estimated glomerular filtration rate (eGFR) fell below the following cut-offs: 60 mL/min/1.73 m chronic kindney disease (CKD) and 15 mL/min/1.73 m (ESKD) for primary scenarios, 45 mL/min/1.73 m (CKD Stage 3b) and 30 mL/min/1.73 m (CKD Stage 4) for additional scenarios. Predictive factors included age, sex and serum creatinine, which are components of eGFR, and MetS factors as follows: body mass index (BMI), glycated haemoglobin A1c (HbA1c), triglycerides (TG) and systolic blood pressure (SBP).
Significant increases in hazard ratios (HRs) for BMI, HbA1c, TG and SBP were observed for primary and additional cut-offs. BMI, HbA1c and TG showed progressively stronger HR increases with advancing stages. The model for all scenarios demonstrated goodness of fit with the high C-statistics.
This study highlights the necessity of a comprehensive evaluation of MetS factors in CKD risk assessment and shows the model using annual HC data can identify CKD progression effectively and accurately. A risk assessment approach considering multiple CKD stages will be crucial for early intervention and disease prevention strategies.
目的/引言：虽然已有关于重度代谢综合征（MetS）患者肾脏疾病（KD）的研究报道，但对未确诊的MetS个体的研究有限。本研究旨在调查日本个体MetS早期阶段的KD机制，以建立适用于特定健康指导的准确KD预测模型，该模型使用年度健康检查（HC）数据。
使用包含过去10年HC和理赔数据的国保数据库进行Cox回归分析。生存时间定义为观察期内从首次HC到估计肾小球滤过率（eGFR）降至以下临界值的时间段：主要场景下，慢性肾脏病（CKD）为60 mL/min/1.73 m²，终末期肾病（ESKD）为15 mL/min/1.73 m²；附加场景下，CKD 3b期为45 mL/min/1.73 m²，CKD 4期为30 mL/min/1.73 m²。预测因素包括年龄、性别和血清肌酐（eGFR的组成部分）以及以下MetS因素：体重指数（BMI）、糖化血红蛋白A1c（HbA1c）、甘油三酯（TG）和收缩压（SBP）。
在主要和附加临界值下，观察到BMI、HbA1c、TG和SBP的风险比（HR）显著增加。BMI、HbA1c和TG随着阶段进展显示出HR增加逐渐增强。所有场景的模型均显示出与高C统计量的良好拟合度。
本研究强调了在CKD风险评估中对MetS因素进行综合评估的必要性，并表明使用年度HC数据的模型可以有效且准确地识别CKD进展。考虑多个CKD阶段的风险评估方法对于早期干预和疾病预防策略至关重要。
