The American journal of the medical sciences · 2026
Glycolysis, inflammation, and mitochondrial dysfunction: new perspectives on the pathogenesis of hypertension.
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Cao Runmin, Zhang Yurun, Cao Ling, Jiang Honghe
Аннотация
BACKGROUND: Recent studies show enhanced glycolysis is linked to chronic diseases, but its relationship with hypertension is unclear. This study explores glycolysis-hypertension links and Pan-Immune Inflammation Value (PIV) and mitochondrial dysfunction chain mediation.
METHODS: Data were derived from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, with 8419 participants included after excluding missing data. Weighted multivariate logistic regression was employed to analyze the independent association between glycolysis and hypertension. Restricted cubic spline curves and threshold effect analysis were used to evaluate the nonlinear relationship between LDH and hypertension. Chain mediation analysis assessed the mediating effects of PIV and mitochondrial dysfunction. Machine learning models predicted hypertension, with three interpretable methods (permutation feature importance, Partial Dependence Plots, and SHAP) embedded in the best-performing model.
RESULTS: After adjusting for confounding factors, both continuous and quartile-based LDH levels showed significant positive correlations with hypertension. A U-shaped association was found, with an inflection point (ln-LDH) of 4.533. Further mediation analysis showed that PIV and mitochondrial dysfunction partially mediated the association between LDH and hypertension, with the indirect effects through PIV alone and mitochondrial dysfunction alone accounting for 2.91% and 6.09% of the total effect, respectively. In addition, the serial indirect effect through PIV and subsequently mitochondrial dysfunction accounted for 0.20% of the total effect. The XGB model had the best predictive performance for hypertension, with LDH, PIV, and MMA being important predictive variables.
CONCLUSIONS: This study emphasizes the important roles of inflammation and mitochondrial dysfunction in the relationship between glycolysis and hypertension.
METHODS: Data were derived from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, with 8419 participants included after excluding missing data. Weighted multivariate logistic regression was employed to analyze the independent association between glycolysis and hypertension. Restricted cubic spline curves and threshold effect analysis were used to evaluate the nonlinear relationship between LDH and hypertension. Chain mediation analysis assessed the mediating effects of PIV and mitochondrial dysfunction. Machine learning models predicted hypertension, with three interpretable methods (permutation feature importance, Partial Dependence Plots, and SHAP) embedded in the best-performing model.
RESULTS: After adjusting for confounding factors, both continuous and quartile-based LDH levels showed significant positive correlations with hypertension. A U-shaped association was found, with an inflection point (ln-LDH) of 4.533. Further mediation analysis showed that PIV and mitochondrial dysfunction partially mediated the association between LDH and hypertension, with the indirect effects through PIV alone and mitochondrial dysfunction alone accounting for 2.91% and 6.09% of the total effect, respectively. In addition, the serial indirect effect through PIV and subsequently mitochondrial dysfunction accounted for 0.20% of the total effect. The XGB model had the best predictive performance for hypertension, with LDH, PIV, and MMA being important predictive variables.
CONCLUSIONS: This study emphasizes the important roles of inflammation and mitochondrial dysfunction in the relationship between glycolysis and hypertension.