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. 2025 May 19:16:1524786.
doi: 10.3389/fendo.2025.1524786. eCollection 2025.

Association between triglyceride-glucose index and the risk of cardiometabolic diseases in metabolically healthy obese individuals: a prospective cohort study

Yanjuan Chen #  1 , Weiqiang Wu #  2 , Zefeng Cai #  2 , Kuangyi Wu  2 , Huancong Zheng  2 , Peng Fu  2 , Yuxian Wang  2 , Xianxuan Wang  3 , Yulong Lan  2 , Shuohua Chen  4 , Shouling Wu  4 , Youren Chen  2
Affiliations

Association between triglyceride-glucose index and the risk of cardiometabolic diseases in metabolically healthy obese individuals: a prospective cohort study

Yanjuan Chen et al. Front Endocrinol (Lausanne). .

Abstract

Background: Metabolically healthy obese (MHO) individuals meet the criteria for obesity with normal blood glucose and lipid metabolism parameters, absence of hypertension, and no concurrent cardiovascular diseases. However, the association between the triglyceride-glucose (TyG) index and the risk of cardiometabolic disease (CMD) in MHO individuals remains unclear.

Methods and results: This study included obese individuals who underwent health examinations at Kailuan Group from 2006 to 2010, whom without a history of hypertension, diabetes, hyperlipidemia, cardiovascular disease, as the study participants. A total of 4750 participants were included in this study. The TyG index was calculated as ln[TG (mg/dL) × FPG (mg/dL)/2] and divided into four groups based on quartiles: Q1 group (<8.18); Q2 group (8.18-8.41); Q3 group (8.42-8.62); Q4 group (≥8.63). The Cox proportional hazards model was used to assess the relationship between the TyG index and risk of CMD incidence. During a median follow-up period of 11 (IQR 10.3, 11.2) years, 826 participants experienced CMD, among whom 131 participants developed coronary heart disease, 215 participants developed stroke, and 542 participants developed diabetes. After adjusting for multiple confounding factors, compared with the Q1 group, the adjusted HRs (95% CI) for CMD in the Q2-Q4 groups were 1.33 (1.03, 1.65), 1.37 (1.04, 1.82), and 2.04 (1.56, 2.68) (P<0.0001). A similar trend was found in the subtypes of CMD in coronary heart disease, stroke, and diabetes. Restrictive cubic spline analysis revealed a linear dose-response relationship between the TyG index and the risk of CMD.

Conclusions: A high TyG index increases the risk of CMD in MHO individuals. Monitoring and maintaining an appropriate TyG index may contribute to the prevention of CMD risk in MHO individuals.

Keywords: cardiometabolic disease; coronary heart disease; diabetes; metabolically healthy obese; stroke; triglyceride-glucose index.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart for the inclusion of participants in the study.
Figure 2
Figure 2
Kaplan–Meier analysis of TyG index and incidence rate of CMD in MHO participants.
Figure 3
Figure 3
The associations of TyG index with risk of CMD in restrictive cubic spline analysis. Caption: Cox regression models with restricted cubic splines were fitted to the data with three knots at the 10th, 50th, and 90th percentiles of the TyG index. The solid line represents the point estimate of the TyG index with the risk of CMD and the shaded part represents the 95% CI estimate. Covariates in the model included age, sex, smoking status, drinking habits, physical activity habits, education level, family history of CMD, SBP, FPG, HDL-C, LDL-C, hs-CRP, BMI, UA, and eGFR.
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References

    1. Collaboration NCDRF . Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet. (2024) 403:1027–50. doi: 10.1016/S0140-6736(23)02750-2 - DOI - PMC - PubMed
    1. Collaborators GBDRF . Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. (2020) 396:1223–49. doi: 10.1016/S0140-6736(20)30752-2 - DOI - PMC - PubMed
    1. Kivimaki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. . Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120–813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health. (2017) 2:e277–e85. doi: 10.1016/S2468-2667(17)30074-9 - DOI - PMC - PubMed
    1. Han Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, et al. . Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. (2021) 42:3374–84. doi: 10.1093/eurheartj/ehab413 - DOI - PMC - PubMed
    1. Emerging Risk Factors C. Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, et al. . Association of cardiometabolic multimorbidity with mortality. JAMA. (2015) 314:52–60. doi: 10.1001/jama.2015.7008 - DOI - PMC - PubMed

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