Associations of C-reactive protein, triglyceride–glucose index, and the C-reactive protein–triglyceride glucose index with multistate trajectories in the cardiovascular–renal–diabetes cluster

TL;DR

This study shows that combining a metabolic marker (TyG) and an inflammatory marker (CRP) into a single index (CTI) better predicts the risk of developing and progressing through cardiovascular, renal, and diabetes-related diseases.

Contribution

The CTI index is introduced as a novel composite biomarker that outperforms individual markers in predicting disease trajectories in the CRD cluster.

Findings

CTI was associated with higher risks of CAD, T2DM, CKD, and multimorbidity compared to CRP and TyG alone.

CTI showed nonlinear associations in baseline-to-disease transitions but linear associations during progression to multimorbidity.

Participants with both high CRP and high TyG had the greatest risks across all outcomes.

Abstract

To investigate the associations of C-reactive protein (CRP), triglyceride–glucose (TyG) index, and their composite—the CRP–TyG index (CTI)—with sequential trajectories within the cardiovascular–renal–diabetes (CRD) cluster, including incident coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and multimorbidity.

We analyzed 333,698 participants from the UK Biobank who were free of CAD, T2DM, and CKD at baseline. CRP and TyG were assessed individually and jointly through the CTI. Multistate Cox models were applied to evaluate six predefined transitions within the CRD cluster, with multimorbidity defined as the coexistence of at least two of CAD, T2DM, and CKD. Potential nonlinearity was assessed using restricted cubic splines, and time-dependent effects were examined with piecewise analyses. Joint exposure analyses assessed synergistic effects of CRP and TyG, while receiver operating characteristic (ROC) curves compared the predictive performance of CRP, TyG, their combination, and CTI. Subgroup and sensitivity analyses were performed to test heterogeneity and robustness.

During a median follow-up of 15.31 years (IQR, 14.54–16.03 years), CTI was consistently associated with higher risks of CAD (HR per 1-SD: 1.23, 95% CI: 1.21–1.25), T2DM (1.88, 95% CI: 1.84–1.92), CKD (1.22, 95% CI: 1.19–1.25), and multimorbidity (1.59, 95% CI: 1.55–1.64), outperforming CRP and TyG individually. CTI exhibited trajectory-specific heterogeneity, with nonlinear associations observed in most baseline-to-disease transitions (P for nonlinearity <0.05) and predominantly linear associations during progression from single diseases to multimorbidity (all P for nonlinearity >0.05). Moreover, higher CTI was associated with a time-dependent cumulative increase in multimorbidity risk. In joint exposure analyses, participants with both high CRP and high TyG had the greatest risks across outcomes, including CAD (HR 1.48, 95% CI: 1.40–1.56), CKD (HR 1.52, 95% CI: 1.40–1.64), T2DM (HR 3.63, 95% CI: 3.35–3.93), and multimorbidity (HR 2.64, 95% CI: 2.47–2.82).

CRP, TyG, and CTI were strongly associated with both the onset and progression of the cardiovascular–renal–diabetes cluster. By integrating metabolic and inflammatory risk signals, CTI outperformed its individual components, underscoring its clinical utility for refined risk stratification and for guiding early, stage-specific prevention strategies.

CTI integrates metabolic (TyG) and inflammatory (CRP) signals to capture shared mechanisms within the cardiovascular–renal–diabetes cluster. Multistate analyses depict transitions from baseline to first incident CAD, T2DM, or CKD and subsequent progression to multimorbidity, showing higher CTI-associated risks across trajectories and improved discrimination compared with CRP, TyG, and their combination. CRP, C-reactive protein; TyG, triglyceride–glucose index; CTI, C-reactive protein–triglyceride–glucose index; T2DM, type 2 diabetes mellitus; CAD, coronary artery disease; CKD, chronic kidney disease.Infographic diagram with two sections. The top section outlines multimorbidity involving type 2 diabetes mellitus, coronary artery disease, and chronic kidney disease via insulin resistance and inflammation, introducing CTI as a biomarker combining TYG and CRP. The bottom section presents a multi-state model flowchart studying disease transitions using hazard ratios and sample sizes, plus four ROC curve charts comparing performance of CTI versus single biomarkers for predicting diseases and comorbidity.

CTI integrates metabolic (TyG) and inflammatory (CRP) signals to capture shared mechanisms within the cardiovascular–renal–diabetes cluster. Multistate analyses depict transitions from baseline to first incident CAD, T2DM, or CKD and subsequent progression to multimorbidity, showing higher CTI-associated risks across trajectories and improved discrimination compared with CRP, TyG, and their combination. CRP, C-reactive protein; TyG, triglyceride–glucose index; CTI, C-reactive protein–triglyceride–glucose index; T2DM, type 2 diabetes mellitus; CAD, coronary artery disease; CKD, chronic kidney disease.

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