Relationship between cardiometabolic index and worsening renal function in T2DM: Insights from machine learning, growth mixture modelling, and development of a web-based risk prediction tool.
Journal:
Diabetes, obesity & metabolism
Published Date:
Dec 22, 2025
Abstract
AIM: Worsening renal function (WRF) is a common and serious complication of type 2 diabetes mellitus (T2DM), contributing to adverse clinical outcomes. Metabolic dysregulation is considered a key driver of its onset and progression. The cardiometabolic index (CMI), a novel marker of metabolic status, has recently been proposed as a potential predictor; however, its utility in predicting WRF remains unclear. MATERIALS AND METHODS: A total of 10 094 participants from the Action to Control Cardiovascular Risk in Diabetes trial were included in the final analysis and external validation was performed using data from an independent, single-centre Chinese cohort. WRF was defined as either a doubling of serum creatinine from baseline or a decline in estimated glomerular filtration rate (eGFR) greater than 20 mL/min/1.73 m2 during follow-up. The Boruta algorithm and random forest-based recursive feature elimination were sequentially applied for feature selection, and six machine learning algorithms were implemented to construct predictive models. SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Restricted cubic splines (RCS) were applied to examine the dose-response relationship between CMI and WRF, and a growth mixture model (GMM) was used to identify distinct CMI trajectories across follow-up visits at baseline, 12, 24, 36, 48, and 60 months. Cox proportional hazards regression models and cumulative incidence curves were used to evaluate the association between CMI and WRF across subgroups. A web-based visualisation tool was further used to enhance model accessibility. RESULTS: After two-step feature selection, six variables were incorporated into the final machine learning model: CMI, eGFR, age, fasting plasma glucose, urinary albumin (UAlb), and urinary creatinine. All six developed models showed stable and favourable performance, with the XGBoost algorithm achieving the best results. SHAP analysis indicated that higher eGFR was the most influential predictor. A web-based visualisation tool was developed to facilitate interactive exploration of model predictions at the individual level (https://t2dm-wrf-prediction.streamlit.app/). RCS curves revealed a strong positive association between CMI and WRF after multiple adjustments. Participants in the highest CMI tertile had a 16% higher risk of WRF compared with those in the lowest tertile (adjusted HR: 1.16; 95% CI: 1.09-1.24), and significant interactions were observed among female participants, older individuals, and those assigned to the standard glycemic control arm. Using the GMM, three distinct CMI trajectory groups were identified: 'low-stable,' 'moderate-stable,' and 'high-increasing.' Cumulative incidence curves further showed that both higher baseline CMI and membership in the 'high-increasing' group were associated with substantially elevated WRF risk. CONCLUSIONS: Elevated CMI is positively associated with WRF in patients with T2DM, and its incorporation into predictive models may improve early identification of high-risk individuals.
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