# Triglyceride-glucose index and its derivatives as emerging biomarkers of insulin resistance for prognostic evaluation of cardiovascular-kidney-metabolic syndrome: mechanisms and clinical applications

## TL;DR

This paper reviews how the triglyceride-glucose index and related biomarkers can help predict and manage cardiovascular-kidney-metabolic syndrome by identifying insulin resistance.

## Contribution

The paper introduces and evaluates the clinical potential of the TyG index and its derivatives as non-invasive biomarkers for insulin resistance in CKM Syndrome.

## Key findings

- The TyG index effectively predicts CKM-related outcomes by integrating fasting glucose and triglycerides.
- Derivative indices like dynamic TyG improve predictive accuracy by adding metabolic parameters.
- Standardized thresholds and population-specific studies are needed to enhance clinical utility.

## Abstract

Cardiovascular-Kidney-Metabolic Syndrome (CKM Syndrome) is a complex, multi-organ disorder characterized by metabolic dysfunction, involving the cardiovascular, renal, and metabolic systems. The components of CKM Syndrome exhibit significant interactions, driving the progression of multi-organ dysfunction. Insulin resistance (IR), a key driver of metabolic disturbances, plays a central role in the pathogenesis of CKM Syndrome. Traditional methods for assessing IR are often invasive and impractical for clinical and large-scale research use. Therefore, novel biomarkers, such as the triglyceride-glucose (TyG) index and its derivatives, have emerged as promising tools for identifying high-risk populations in CKM Syndrome. This review aims to provide a comprehensive analysis of the current application and potential of emerging biomarkers for IR in the prognostic evaluation of CKM. We focus on the clinical pathophysiological mechanisms underlying IR and its role in CKM, including disturbances in glucose and lipid metabolism, inflammatory responses, oxidative stress, endothelial dysfunction, and renal injury. The TyG index, which integrates fasting blood glucose and triglycerides, has shown significant potential in predicting CKM-related outcomes. Furthermore, derivative indices, such as the dynamic TyG and combined TyG index, offer enhanced predictive capability by incorporating additional physiological and metabolic parameters. Despite the promising applications of the TyG index and its derivatives, several limitations exist, including the lack of standardized thresholds, variability across populations, and challenges in dynamic monitoring. Future research should focus on elucidating the molecular mechanisms of IR, conducting large-scale multi-center studies, and developing individualized monitoring models to improve the clinical utility of these biomarkers. The TyG index holds significant potential for early identification, risk stratification, and intervention in CKM, offering a new strategy for improving patient outcomes and transforming chronic disease management.