The synergistic effect of the triglyceride-glucose index and serum uric acid on the prediction of major adverse cardiovascular events after coronary artery bypass grafting: a multicenter retrospective cohort study

Wu, Zhiyang; Cheng, Cheng; Sun, Xiangfei; Wang, Juan; Guo, Dachuan; Chen, Sha; Zhang, Yerui; Liu, Xiaoyu; Liu, Li; Zhang, Cheng; Yang, Jianmin

doi:10.1186/s12933-023-01838-z

Cited by 9 publications

(4 citation statements)

References 61 publications

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“…A recent study reported that either of the factors, hyperuricemia or elevated TyG index, could independently predict the risk of major adverse cardiovascular event (MACE) in patients underwent coronary artery bypass grafting (CABG), HUA and High TyG index showed a synergistic interaction [38], consistent with our results.…”

Section: Discussionsupporting

confidence: 91%

Triglyceride–glucose index and serum uric acid on the prediction of major adverse cardiovascular and cerebrovascular events after PCI: a retrospective cohort study

Huo,

Hu,

Zhang

et al. 2024

Preprint

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Background：Both serum uric acid (SUA) levels and TyG index have been reported to associate with adverse outcomes in patients with coronary artery disease. Limited evidence has focus on the combined and mediating effects of TyG on the association between SUA and cardiovascular and cerebrovascular events in patients underwent percutaneous coronary intervention (PCI). Methods：In this retrospective cohort study, a total of 6923 patients who underwent PCI were included. The patients were grouped based on the cut-off value of the TyG index and the sex-specifc criteria of hyperuricemia (HUA). The primary end point was major adverse cardiovascular and cerebrovascular events (MACCE), a composite of cardiac death, myocardial infarction, stroke, and target lesion revascularization. Cox regression analysis was conducted to estimate the association of TyG index and SUA with clinical outcome. A mediation analysis was performed to assess whether TyG index mediates the relationship between SUA and MACCE. Results：During a median of 693 days follow-up, 756 (10.9%) patients experienced MACCE. Compared with the Non-HUA/L-TyG group, the HUA/H-TyG group experienced the highest MACCE risk[hazard ratio(HR): 1.455,95% confidence interval(CI): 1.177–1.799, p < 0.001], followed by the HUA/L-TyG group(HR: 1.342, 95% CI: 1.093–1.647, p = 0.005) and Non-HUA/H-TyG (HR: 1.226, 95% CI: 1.007–1.493, p = 0.043). The TyG index significantly mediated 50.25% of the relationship between SUA and MACCE (p < 0.001). Conclusions：In patients undergone PCI, the TyG index and HUA synergically increased the risk of cardiovascular and cerebrovascular events, and TyG index partially mediated the association between HUA and clinical outcomes, which emphasizes that combining TyG and SUA can help identify high risk patients.

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Section: Discussionsupporting

confidence: 91%

Triglyceride–glucose index and serum uric acid on the prediction of major adverse cardiovascular and cerebrovascular events after PCI: a retrospective cohort study

Huo,

Hu,

Zhang

et al. 2024

Preprint

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“…Intriguingly, the two predictivefactorsshowed a synergistic interaction. As such, the highest risk of reporting MACE was noted among the subjects presenting with increased TyG index and serum uric acid levels simultaneously, as compared to those with lower levels of either of these factors ( 64 ). These reports confirm the added value of considering both insulin resistance and hyperuricemia indicators when assessing risk for CVDs and comorbid complications.…”

Section: Discussionmentioning

confidence: 99%

Insulin resistance surrogate markers and risk of hyperuricemia among patients with and without coronary artery disease: a cross-sectional study

Ghorbani,

Mirmohammadali,

Shoaibinobarian

et al. 2023

Front. Nutr.

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BackgroundAlthough emerging evidence emphasizes the associations between both insulin resistance and hyperuricemia with coronary artery disease (CAD) risk, no definite relationship has yet been established. In this respect, time-efficient and affordable methods to estimate insulin resistance (IR) status, and to predict risk of hyperuricemia, are needed. Thus, the goal of this investigation was to examine the associations between IR, as assessed by novel surrogate markers [triglyceride-glucose (TyG) and TyG–body mass index (TyG-BMI)], and risk of hyperuricemia in patients with and without diagnosed CAD.MethodsThis cross-sectional study used data from the medical records of 1,170 patients who were referred to the cardiology outpatient clinic. Medical records, anthropometrics, and serum analytes were determined at the initial visit. Hyperuricemia was defined as serum uric acid ≥ 5.6 mg/dL. IR was estimated through surrogate markers (TyG and TyG-BMI). Multiple regression analysis was performed to assess the relationship between these indices and odds of hyperuricemia among patients with and without CAD.ResultsOverall, 814 angiographically-confirmed CAD cases (mean age (SD) = 52 (8)yrs) were compared with 356 patients without CAD (mean age (SD) = 48 (8)yr). There were positive associations between TyG and TyG-BMI indices and odds of hyperuricemia in CAD patients after controlling for confounders (adjusted odds ratio (aOR) = 1.60; 95%CI: 1.02–2.51; p-value = 0.036; and aOR = 1.83; 95%CI: 1.24–2.70; p-value = 0.002, third tertiles for TYG and TYG-BMI, respectively).ConclusionThe present findings suggest that higher levels of the IR surrogate markers, TyG and TyG-BMI, are associated with higher odds of hyperuricemia in patients with CAD. However, given the cross-sectional design of this study, the sensitivity and specificity of these novel markers could not be determined for confirming the diagnosis of IR and hyperuricemia, further studies are needed to determine such outcomes and to confirm the current findings.

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“…Guo Y et al found that inulin, a non-digestible dietary fiber, can significantly ameliorate hyperuricemia in mice by studying the effect of inulin on mice with hyperuricemia due to knockout of the UA oxidase gene [ 39 ]. In a multicenter retrospective study, Wu Z et al pointed out that the TyG index, a novel and reliable surrogate for IR, and hyperuricemia are both important metabolic risk factors, that are closely related to each other and contribute to each other through multiple mechanisms [ 40 ]. Zhao Q et al demonstrated a nonlinear relationship between serum UA and TyG index in children with short stature, which remained nonlinear after gender stratification [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

Cao,

2024

Nutr Metab (Lond)

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Background Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. Methods Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. Results An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798–0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. Conclusion The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

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The synergistic effect of the triglyceride-glucose index and serum uric acid on the prediction of major adverse cardiovascular events after coronary artery bypass grafting: a multicenter retrospective cohort study

Cited by 9 publications

References 61 publications

Triglyceride–glucose index and serum uric acid on the prediction of major adverse cardiovascular and cerebrovascular events after PCI: a retrospective cohort study

Triglyceride–glucose index and serum uric acid on the prediction of major adverse cardiovascular and cerebrovascular events after PCI: a retrospective cohort study

Insulin resistance surrogate markers and risk of hyperuricemia among patients with and without coronary artery disease: a cross-sectional study

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

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