Frontiers in Endocrinology (Feb 2024)

Association between TyG index trajectory and new-onset lean NAFLD: a longitudinal study

  • Haoshuang Liu,
  • Haoshuang Liu,
  • Jingfeng Chen,
  • Jingfeng Chen,
  • Qian Qin,
  • Su Yan,
  • Youxiang Wang,
  • Youxiang Wang,
  • Jiaoyan Li,
  • Jiaoyan Li,
  • Suying Ding,
  • Suying Ding

DOI
https://doi.org/10.3389/fendo.2024.1321922
Journal volume & issue
Vol. 15

Abstract

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ObjectiveThe purpose of this manuscript is to identify longitudinal trajectories of changes in triglyceride glucose (TyG) index and investigate the association of TyG index trajectories with risk of lean nonalcoholic fatty liver disease (NAFLD).MethodsUsing data from 1,109 participants in the Health Management Cohort longitudinal study, we used Latent Class Growth Modeling (LCGM) to develop TyG index trajectories. Using a Cox proportional hazard model, the relationship between TyG index trajectories and incident lean NAFLD was analyzed. Restricted cubic splines (RCS) were used to visually display the dose-response association between TyG index and lean NAFLD. We also deployed machine learning (ML) via Light Gradient Boosting Machine (LightGBM) to predict lean NAFLD, validated by receiver operating characteristic curves (ROCs). The LightGBM model was used to create an online tool for medical use. In addition, NAFLD was assessed by abdominal ultrasound after excluding other liver fat causes.ResultsThe median age of the population was 46.6 years, and 440 (39.68%) of the participants were men. Three distinct TyG index trajectories were identified: “low stable” (TyG index ranged from 7.66 to 7.71, n=206, 18.5%), “moderate stable” (TyG index ranged from 8.11 to 8.15, n=542, 48.8%), and “high stable” (TyG index ranged from 8.61 to 8.67, n=363, 32.7%). Using a “low stable” trajectory as a reference, a “high stable” trajectory was associated with an increased risk of lean-NAFLD (HR: 2.668, 95% CI: 1.098-6.484). After adjusting for baseline age, WC, SBP, BMI, and ALT, HR increased slightly in “moderate stable” and “high stable” trajectories to 1.767 (95% CI:0.730-4.275) and 2.668 (95% CI:1.098-6.484), respectively. RCS analysis showed a significant nonlinear dose-response relationship between TyG index and lean NAFLD risk (χ2 = 11.5, P=0.003). The LightGBM model demonstrated high accuracy (Train AUC 0.870, Test AUC 0.766). An online tool based on our model was developed to assist clinicians in assessing lean NAFLD risk.ConclusionThe TyG index serves as a promising noninvasive marker for lean NAFLD, with significant implications for clinical practice and public health policy.

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