Work

Chronic kidney disease risk assessment: Findings from backward‐looking study using annual health check‐up data in Japan

Year: 2025

Type: article

Abstract: 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. Materials and Methods 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 2 chronic kindney disease (CKD) and 15 mL/min/1.73 m 2 (ESKD) for primary scenarios, 45 mL/min/1.73 m 2 (CKD Stage 3b) and 30 mL/min/1.73 m 2 (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). Results 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. Conclusions 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.

Source: Diabetes Obesity and Metabolism

DOI: 10.1111/dom.16390

Authors: Chihaya Fukai, Shumpei Chiba, Takaaki Itoga, Gen Kobayashi, Kohei Kaku

Institution: Kawasaki Medical School

Language: English

Topic: Chronic Kidney Disease and Diabetes

Subfield: Nephrology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,380