A correlation analysis between TyG-derived indices and diabetes mellitus: based on the NHANES database

Diabetes mellitus (DM) is a key global public health issue with rising incidence. The triglyceride-glucose (TyG) index has been widely applied to assess insulin resistance and metabolic abnormalities in recent years. However, the relation of the TyG index with DM is elusive when combined with central obesity indicators. This research was conducted to probe into the relationship between TyG-derived indices and DM. A total of 10,729 participants from the NHANES database were enrolled, of whom 1,984 had DM. The linkage of five TyG-derived indices with DM was examined using a weighted logistic regression model. At the same time, stratified analysis was undertaken on different gender and age subgroups. To evaluate the predictive performance of each indicator, ROC curve analysis was undertaken to examine the predictive capability of different indicators. The findings indicated that all TyG-derived indices were greatly positively linked with the risk of DM. The AUC values of TyG-CI and TyG-WWI were considerably higher than those of the TyG index and other indicators, demonstrating a stronger capability to predict the risk of DM. In subgroup analyses, both TyG-CI and TyG-WWI exhibited high robustness across different populations regardless of gender or age. The indices with the TyG index combined with indicators related to central obesity, especially TyG-CI and TyG-WWI, are effective tools for predicting the risk of DM.

Authors

Institutions

Publication Details

Journal
Endocrine Journal
Published
2025-11-27
DOI
https://doi.org/10.1507/endocrj.ej25-0002
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A correlation analysis between TyG-derived indices and diabetes mellitus: based on the NHANES database

Zhi Ming Cheng, Ting Sun, Xiaohua Yang
Endocrine Journal
Diabetes, Cardiovascular Risks, and Lipoproteins
article

A correlation analysis between TyG-derived indices and diabetes mellitus: based on the NHANES database

Zhi Ming Cheng, Ting Sun, Xiaohua Yang
article en

Abstract

Diabetes mellitus (DM) is a key global public health issue with rising incidence. The triglyceride-glucose (TyG) index has been widely applied to assess insulin resistance and metabolic abnormalities in recent years. However, the relation of the TyG index with DM is elusive when combined with central obesity indicators. This research was conducted to probe into the relationship between TyG-derived indices and DM. A total of 10,729 participants from the NHANES database were enrolled, of whom 1,984 had DM. The linkage of five TyG-derived indices with DM was examined using a weighted logistic regression model. At the same time, stratified analysis was undertaken on different gender and age subgroups. To evaluate the predictive performance of each indicator, ROC curve analysis was undertaken to examine the predictive capability of different indicators. The findings indicated that all TyG-derived indices were greatly positively linked with the risk of DM. The AUC values of TyG-CI and TyG-WWI were considerably higher than those of the TyG index and other indicators, demonstrating a stronger capability to predict the risk of DM. In subgroup analyses, both TyG-CI and TyG-WWI exhibited high robustness across different populations regardless of gender or age. The indices with the TyG index combined with indicators related to central obesity, especially TyG-CI and TyG-WWI, are effective tools for predicting the risk of DM.

Endocrine JournalVol. 73(2)
Second Affiliated Hospital of Zhejiang University (CN)
Openalex Percentile: Top 51%
Diabetes, Cardiovascular Risks, and Lipoproteins
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.