Content area

Abstract

Background

Triglyceride and glucose (TyG) indices have been used as predictors of several chronic diseases. However, there is currently a lack of research that can comprehensively reflect the impact of TyG-related indicators on chronic diseases in middle-aged and elderly populations. The aim of this study was to investigate the relationship of TyG and its related indicators with chronic diseases and their time-dependent predictive ability in the elderly.

Study design

Retrospective observational cohort study using China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 data.

Methods

Based on longitudinal data obtained from the CHARLS from 2011 to 2020, a total of 12,966 participants were included in the study. Participants were stratified into three groups according to their TyG index. Pearson correlation coefficient and Cox model are used to assess the relationship between the TyG index, its parameters, and common chronic diseases, while Harrell’s C-index is used to evaluate their risk prediction capability.

Results

The TyG index and its related indicators exhibit a positive dose-response relationship with the risk of diabetes, heart disease, dyslipidemia, hypertension, and stroke, while demonstrating a negative dose-response relationship with digestive system diseases. Harrell’s C-index results indicated that TyG-WC demonstrates superior predictive performance overall.

Conclusion

The TyG index and its related indicators are significantly correlated with newly onset emerging chronic diseases, with TyG-WC exhibits superior risk prediction performance.

Details

1009240
Location
Title
Evaluating the associations and predictive performance of triglyceride-glucose index and related indicators for chronic diseases in a Chinese cohort
Publication title
PLoS One; San Francisco
Volume
20
Issue
8
First page
e0330711
Number of pages
23
Publication year
2025
Publication date
Aug 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2025-02-13 (Received); 2025-08-05 (Accepted); 2025-08-26 (Published)
ProQuest document ID
3243921477
Document URL
https://www.proquest.com/scholarly-journals/evaluating-associations-predictive-performance/docview/3243921477/se-2?accountid=208611
Copyright
© 2025 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-08-27
Database
2 databases
  • Coronavirus Research Database
  • ProQuest One Academic