Aggregate risk score based on markers of hepatic steatosis, systemic inflammation, and renal stress to predict MACE across the CKM spectrum
B Zhang, C Zhao, R Ran, S Chen, Z Lian, M Liu, X Zheng, T Shi, Y Li, D Wang, W Cai, Y Yan, G LiAbstract
Background
The stage-specific evolution of risk drivers in Cardiovascular-Kidney-Metabolic (CKM) syndrome remains underexplored. This study evaluated the comparative and cumulative prognostic value of Fatty Liver Index (FLI), Cystatin C (CysC), and high-sensitivity C-reactive protein (hsCRP) for Major Adverse Cardiovascular Events (MACE) across CKM stages 0–3.
Methods
We analyzed 382,797 participants from the UK Biobank classified into CKM stages 0–3. Multivariable Cox proportional hazards models were used to assess the risk of incident MACE associated with the three biomarkers. The primary analysis employed a binary cumulative risk strategy, categorizing participants into High-Risk (≥2 elevated biomarkers) and Low-Risk (0–1 factor) groups. Survival analyses were performed with models adjusted for established risk factors. Model discrimination was evaluated using the C-index.
Results
Over a median follow-up of 15.2 years, elevated FLI, CysC, and hsCRP were independently associated with MACE across all CKM stages. The categorical model provided a superior fit compared to the linear model (Chi-square 17,396.04 vs. 17,269.82; P < 0.001). Significant interactions were observed between the binary risk strategy and CKM stages (P for interaction = 0.022), with the highest relative risk observed in the early stages. In Stage 0, high-risk individuals faced a 1.95-fold higher risk of MACE (95% CI 1.45–2.61) and a 2.28-fold higher risk of cardiovascular death (95% CI 1.28–4.06). Adding the cumulative risk strategy to the clinical model improved the C-index from 0.717 to 0.730.
Conclusion
An aggregate score based on FLI, CysC, and hsCRP is a potent, stage-dependent predictor of future MACE risk across the CKM spectrum. By unmasking residual risk in seemingly low-risk Stage 0 and 1 populations, this strategy provides a critical window for biomarker-guided primordial prevention.Figure 1Figure 2