Association between TyG index trajectory and new-onset lean NAFLD : a longitudinal study
Copyright © 2024 Liu, Chen, Qin, Yan, Wang, Li and Ding..
Objective: The 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).
Methods: Using 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.
Results: The 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.
Conclusion: The TyG index serves as a promising noninvasive marker for lean NAFLD, with significant implications for clinical practice and public health policy.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Frontiers in endocrinology - 15(2024) vom: 31., Seite 1321922 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Haoshuang [VerfasserIn] |
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| Links: |
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| Themen: |
Glucose |
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| Anmerkungen: |
Date Completed 14.03.2024 Date Revised 14.03.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.3389/fendo.2024.1321922 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36966390X |
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| 100 | 1 | |a Liu, Haoshuang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Association between TyG index trajectory and new-onset lean NAFLD |b a longitudinal study |
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| 500 | |a Date Revised 14.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Liu, Chen, Qin, Yan, Wang, Li and Ding. | ||
| 520 | |a Objective: The 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) | ||
| 520 | |a Methods: Using 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 | ||
| 520 | |a Results: The 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 | ||
| 520 | |a Conclusion: The 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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| 650 | 4 | |a latent class growth model | |
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| 700 | 1 | |a Wang, Youxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jiaoyan |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Suying |e verfasserin |4 aut | |
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