# Combining triglyceride glucose-body mass index and high-sensitivity C-reactive protein to predict cardiovascular disease: results from a national cohort and a clinical verified cohort

## TL;DR

This study shows that combining two markers—TyG-BMI and hsCRP—can better predict cardiovascular disease risk than using either alone.

## Contribution

The novel contribution is demonstrating how TyG-BMI and hsCRP together improve CVD risk prediction in both general and post-PCI populations.

## Key findings

- High TyG-BMI and high hsCRP levels are strongly associated with increased CVD risk (HR: 1.38).
- Adding TyG-BMI to baseline models significantly improves predictive performance in a clinical cohort.
- A non-linear relationship exists between TyG-BMI, hsCRP, and CVD risk.

## Abstract

Accumulating evidence highlights a strong association between insulin resistance (IR) and inflammation in the context of cardiovascular disease (CVD). However, the combined impact of both factors on the risk of developing CVD remains underexplored. This study aims to investigate how inflammation and insulin resistance interact to influence the risk of incident CVD.

A total of 6,534 adults from the CHARLS were included. The primary endpoint was the occurrence of cardiovascular disease (CVD), defined as a composite of stroke and cardiac events. Participants were categorized according to the median values of two key markers: the triglyceride glucose-body mass index (TyG-BMI index) and high-sensitivity C-reactive protein (hsCRP). Cox proportional hazard models assessed the associations between TyG-BMI index, hsCRP, and the endpoints. To validate the reliability of the results, we clinically enrolled 805 patients who had undergone percutaneous coronary intervention (PCI).

In CHARLS, Multivariate Cox regression analysis revealed a significant association between elevated TyG-BMI index and hsCRP levels and an increased risk of CVD. Participants in the high TyG-BMI and high hsCRP group demonstrated a substantially higher risk of CVD (HR: 1.38, 95% CI: 1.18–1.62, P < 0.001) compared to those with low TyG-BMI and low hsCRP. Restricted cubic spline (RCS) analysis indicated a non-linear, positive relationship between both the TyG-BMI index and hsCRP levels with the risk of developing CVD. In the validation cohort, we also observed that the high TyG-BMI group had higher risks of MACCE, stroke, and cardiac events (all P < 0.05), and that the predictive performance of the model was significantly improved after adding TyG-BMI to the baseline model.

These results emphasize the importance of simultaneously considering both TyG-BMI index and hsCRP levels in risk stratification for CVD, highlighting their combined potential to enhance early identification of high-risk individuals. Importantly, TyG-BMI also demonstrated significant predictive value for adverse outcomes in patients after PCI.