# Dynamic trajectories of the triglyceride-glucose index link to all-cause hospital mortality in patients with hypertension and kidney failure: a multicenter study

## TL;DR

Tracking changes in the triglyceride-glucose index during hospitalization helps predict mortality in patients with hypertension and kidney failure.

## Contribution

Introduces a time-weighted TyG metric and shows how dynamic TyG patterns predict hospital mortality in cardiorenal patients.

## Key findings

- Four distinct TyG trajectories were identified, with rapidly increasing and persistent high groups showing higher mortality.
- A novel time-weighted TyG metric (WATyG) was linearly associated with increased mortality risk.
- Lower mortality risks were observed in patients with rapidly decreasing or stable low TyG trajectories.

## Abstract

Chronic kidney disease and hypertension form a vicious cardiorenal cycle, exacerbating metabolic dysfunction and mortality. The triglyceride-glucose (TyG) index, a surrogate for insulin resistance, has shown prognostic value in cardiovascular and renal diseases. Previous research analyzed single-timepoint TyG, ignoring longitudinal trajectories during hospitalization. We aimed to investigate TyG trajectories and their association with hospital mortality in patients with hypertension and kidney failure (KF).

Patients diagnosed with hypertension and KF were retrospectively retrieved from MIMIC-IV and a private dataset. Patients were clustered into four TyG trajectory groups using K-means clustering. A novel time-weighted average TyG (WATyG) metric was developed to quantify cumulative metabolic exposure. Logistic regression, restricted cubic spline (RCS) models, and subgroup analyses examined associations between TyG dynamics and mortality.

A total of 2,038 patients from MIMIC-IV and 1,266 from a private dataset were analyzed, with mortality rates of 28.41% and 7.03%, respectively. Four TyG trajectories were identified: rapidly increasing (Cluster 1), rapidly decreasing (Cluster 2), persistent high (Cluster 3), and stable low (Cluster 4). Clusters 1 and 3 had significantly higher mortality rates than Clusters 2 and 4 (all P<0.001). In MIMIC-IV, mortality rates were 38.8%/35.0% for Clusters 1/3 versus 22.6%/22.7% for Clusters 2/4, while the private dataset showed rates of 18.5%/7.6% (Clusters 1/3) versus 5.5%/4.0% (Clusters 2/4). Using Cluster 1 as reference in the adjusted model, Cluster 2 (OR 0.546, P=0.007) and Cluster 4 (OR 0.492, P<0.001) showed lower mortality risks in MIMIC-IV, with consistent trends in the private dataset. WATyG was linearly associated with an increased risk of mortality (OR 1.505, P<0.001 in MIMIC-IV).

Dynamic TyG trajectories are linked to mortality risk in patients with hypertension and KF. WATyG improves risk stratification via cumulative metabolic exposure. Longitudinal TyG monitoring holds potential value for optimizing clinical decision-making by enabling continuous assessment of metabolic risk.