Evidence mapPaperPMID 41470001Full record

ArticleLipids in health and disease2025

Comparison of the TyG index, TyG-traditional obesity indices, and TyG-novel obesity indices: does increased complexity help in predicting cardiometabolic multimorbidity?

Fanzhi Zhang, Bin Zhang, Xinfang Huang, Zhenyu Wang, Juan Wang, Houhui Lan, Guobo Xie, Wei Wang, Yang Zou, Chao Wang

Abstract readComparative Study
In one paragraph

Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

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14citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Fanzhi ZhangDepartment of Cardiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Bin ZhangDepartment of Cardiology, Lu' an Hospital of Anhui Medical University, Lu' an, Anhui Province, China.
Xinfang HuangJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Zhenyu WangJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Juan WangJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Houhui LanJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Guobo XieDepartment of Cardiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.
Wei WangJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China. wwangcvai@163.com.
Yang ZouJiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China. jxyxyzy@163.com.
Chao WangDepartment of Cardiology, Lu' an Hospital of Anhui Medical University, Lu' an, Anhui Province, China. w2974040609@163.com.

Funding

National Natural Science Foundation of China 81670370Natural Science Foundation of Jiangxi Province 20224ACB206004Natural Science Foundation of Jiangxi Province 20232BAB216004Science and Technology Project of Jiangxi Provincial Health Commission 202510148
6 · The paper itself

Abstract

backgroundThe triglyceride-glucose (TyG) index is an important determinant influencing the incidence of cardiometabolic multimorbidity (CMM). However, it remains unclear whether combining the TyG index with novel obesity indices (CVAI/BRI/ABSI/WWI) can improve the risk stratification of CMM. This study aimed to systematically compare the incremental risk assessment and predictive value of the TyG index, TyG-traditional obesity indices (TyG-WC/TyG-WHtR/TyG-BMI), and TyG-novel obesity indices (TyG-CVAI/TyG-BRI/TyG-ABSI/TyG-WWI) for CMM.

methodsTrajectory changes and cumulative exposure of TyG-related parameters were quantified using repeated measurements from the CHARLS cohort (n = 3,885). The study endpoint CMM was defined as a comorbid condition encompassing two or more cardiometabolic diseases, namely diabetes, stroke and heart diseases. A multi-model analytical strategy was employed to evaluate the associations between TyG-related parameters and CMM, as well as the contribution of their components. The net reclassification index and integrated discrimination improvement were employed to evaluate the improvement in predictive performance of these indices.

resultsOver a median follow-up period of 8 years, we identified a linear positive association between TyG-related parameters and CMM, with the cumulative effects of glucose and obesity emerging as the key drivers. Compared with the baseline TyG index, the incremental risk assessment value for CMM improved by 10%-17% (baseline) and 12%-20% (cumulative exposure) for TyG-traditional obesity indices, while the improvement for TyG-novel obesity indices ranged from - 1% to 16% and 5%-19%, respectively. In summary, all TyG-traditional obesity indices demonstrated strong associations with CMM, whereas among the TyG-novel obesity indices, only TyG-CVAI showed a similarly strong association. Furthermore, all TyG-related parameters showed significantly increased hazard ratios in their highest-exposure or poor-control status versus the reference (lowest exposure or good control): TyG-index (1.69/2.05), TyG-WC (2.24/2.28), TyG-WHtR (1.92/2.05), TyG-BMI (1.85/2.27), TyG-CVAI (1.89/2.07), TyG-BRI (1.94/2.08), TyG-ABSI (1.70/1.85), and TyG-WWI (1.97/1.95). Predictive analyses showed that, except for TyG index, TyG-ABSI and TyG-WWI, all other TyG-related parameters provided a certain degree of net improvement compared with the baseline risk model.

conclusionAll eight TyG-related parameters can predict the incidence of CMM. Given their relative simplicity, the TyG-traditional obesity indices demonstrate superior incremental risk assessment and predictive value for CMM compared to the TyG-novel obesity indices and the TyG index, positioning them as promising and more practical tools for clinical practice.

Indexed as

Blood GlucoseCardiovascular DiseasesObesityTriglyceridesAdultAgedBody Mass IndexCardiometabolic Risk FactorsFemaleHumansMaleMiddle AgedMultimorbidityRisk AssessmentBlood GlucoseTriglyceridesCardiometabolic multimorbidityTyG indexTyG-novel obesity indicesTyG-related parametersTyG-traditional obesity indices

Identifiers

PMID41470001
PMCPMC12860121

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.