Background: Early-onset type 2 diabetes mellitus (T2DM) is closely associated with an increased risk of diabetic kidney disease (DKD), and obesity is a well-recognized contributing factor. Traditional measures like body mass index (BMI) have limitations in capturing visceral fat distribution. The A Body Shape Index (ABSI), a newer anthropometric indicator, may provide a more accurate assessment of central obesity. This study investigated the relationship between ABSI and DKD in individuals with early-onset T2DM.
Methods: This cross-sectional study analyzed data from 2,598 patients with early-onset T2DM enrolled at the National Metabolic Management Centers of Yuhuan Second People's Hospital and Taizhou Central Hospital between 2017 and 2024. Multivariate logistic regression models were used to evaluate the association between ABSI and DKD, with additional analyses using restricted cubic splines (RCS) to explore dose-response patterns. Subgroup and interaction analyses were also performed.
Results: Of the participants, 1,030 (39.6%) were diagnosed with DKD. The prevalence of DKD increased across ABSI tertiles: 35.8% in the lowest tertile (T1), 38.5% in the middle tertile (T2), and 44.7% in the highest tertile (T3) (P < 0.001). Higher ABSI was independently associated with a greater risk of DKD (OR = 1.24; 95% CI: 1.05-1.50; P = 0.022) after adjusting for potential confounders. Patients in the highest ABSI tertile had a significantly higher risk of DKD than those in the lowest tertile (OR = 1.24; 95% CI: 1.01-1.52; P = 0.041). RCS analysis showed a positive linear relationship between ABSI and DKD risk (P for non-linearity = 0.139), and the findings were consistent across subgroups.
Conclusion: ABSI is positively and linearly associated with the risk of DKD in patients with early-onset T2DM. This metric may be useful for identifying individuals at higher risk and guiding early preventive strategies.
Keywords: a body shape index; diabetic kidney disease; early-onset; type 2 diabetes; visceral obesity.
Copyright © 2025 Chen, Wang, Feng, Wu, Liu, Liang, Yang and Zheng.