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Background <p>Metabolic syndrome (MetS) is a cluster of risk factors including increased triglycerides, insulin resistance, and hypertension, posing increasing public health challenges. Both cardiorespiratory fitness (CRF) and the Triglyceride-Glucose (TyG) index have been associated with MetS risk independently. However, their combined predictive value remains unclear. This study aims to assess the combined influence of CRF and TyG index on MetS risk in a survey sample.</p> Methods <p>Data from 3742 participants in the National Health and Nutrition Examination Survey (NHANES) in year cycle of 1999–2004 were analyzed. Logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the associations of CRF and TyG index with MetS risk. Subgroup analyses by different CRF, TyG, and disease conditions were conducted to explore interaction effects across different populations. Sensitivity analysis was implemented to verify the robustness of the results. Predictive value was assessed using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and area under the curve (AUC) of receiver operating characteristic (ROC) curve.</p> Results <p>Logistic regression showed that impaired CRF was associated with a 73% higher risk of MetS (Odds Ratio (OR) 1.73; 95% Confidence Interval (CI), 1.23–2.42), while elevated TyG index was associated with a 6.84-fold increased risk (OR 6.84; 95% CI, 2.71–17.29). The combination of impaired CRF and high TyG index showed the highest risk of MetS (OR 11.99; 95% CI, 3.79–37.98). In sensitivity analysis, the results remained similar. Subgroup and interaction analyses further confirmed these findings, showing consistent results across demographic groups and under various analytical conditions. The combined use of CRF and TyG index significantly enhanced the predictive performance (AUC 0.871; 95% CI, 0.856–0.886) and improved model classification capabilities (NRI 0.393; 95% CI, 0.309–0.476; IDI 0.020; 95% CI, 0.014–0.025).</p> Conclusions <p>This study reveals that CRF and TyG index independently predict MetS risk, while their combination demonstrates superior predictive accuracy compared to using either parameter alone. These findings indicate that integrating both CRF and TyG into clinical practice may improve early detection and preventive strategies for MetS.</p>

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The additive effect of cardiopulmonary fitness and triglyceride-glucose index on the risk of metabolic syndrome

  • 期刊论文

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  • 发表日期：

    2025年9月26日

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  • Mengyi Li,
  • Shiqi Wang,
  • Hanbin Li,
  • Wen Zhong,
  • Hongxin Cheng,
  • Quan Wei,
  • Lu Wang

摘要

Background

Metabolic syndrome (MetS) is a cluster of risk factors including increased triglycerides, insulin resistance, and hypertension, posing increasing public health challenges. Both cardiorespiratory fitness (CRF) and the Triglyceride-Glucose (TyG) index have been associated with MetS risk independently. However, their combined predictive value remains unclear. This study aims to assess the combined influence of CRF and TyG index on MetS risk in a survey sample.

Methods

Data from 3742 participants in the National Health and Nutrition Examination Survey (NHANES) in year cycle of 1999–2004 were analyzed. Logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the associations of CRF and TyG index with MetS risk. Subgroup analyses by different CRF, TyG, and disease conditions were conducted to explore interaction effects across different populations. Sensitivity analysis was implemented to verify the robustness of the results. Predictive value was assessed using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and area under the curve (AUC) of receiver operating characteristic (ROC) curve.

Results

Logistic regression showed that impaired CRF was associated with a 73% higher risk of MetS (Odds Ratio (OR) 1.73; 95% Confidence Interval (CI), 1.23–2.42), while elevated TyG index was associated with a 6.84-fold increased risk (OR 6.84; 95% CI, 2.71–17.29). The combination of impaired CRF and high TyG index showed the highest risk of MetS (OR 11.99; 95% CI, 3.79–37.98). In sensitivity analysis, the results remained similar. Subgroup and interaction analyses further confirmed these findings, showing consistent results across demographic groups and under various analytical conditions. The combined use of CRF and TyG index significantly enhanced the predictive performance (AUC 0.871; 95% CI, 0.856–0.886) and improved model classification capabilities (NRI 0.393; 95% CI, 0.309–0.476; IDI 0.020; 95% CI, 0.014–0.025).

Conclusions

This study reveals that CRF and TyG index independently predict MetS risk, while their combination demonstrates superior predictive accuracy compared to using either parameter alone. These findings indicate that integrating both CRF and TyG into clinical practice may improve early detection and preventive strategies for MetS.