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Impact of inflammatory and nutritional parameters on mortality in cardiovascular multimorbidity: a comprehensive
Ziqi Chen1, Aijing Zhu2, Xu Zhu1
1State Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Insights
The systemic inflammation response index (SIRI) is a strong predictor of mortality in patients with cardiovascular multimorbidity (CMM). Higher SIRI levels indicate increased risk for all-cause and cardiovascular death, aiding in risk stratification.
Area of Science:
- Cardiology
- Biomarkers
- Public Health
Background:
- Cardiovascular multimorbidity (CMM) presents a growing global health challenge, linked to premature mortality.
- Systemic inflammation is a key factor in cardiometabolic diseases, but its prognostic value in CMM is not fully understood.
Purpose of the Study:
- To investigate the prognostic implications of inflammatory and nutritional biomarkers in patients with CMM.
- To identify optimal predictors of mortality and develop predictive models for CMM patients.
Main Methods:
- Analysis of 1,928 CMM patients from NHANES and 364 from a Chinese cohort.
- Evaluation of ten inflammatory and nutritional parameters, including the systemic inflammation response index (SIRI).
- Application of multivariable Cox regression, feature selection techniques, nomograms, and machine learning (ML) models for prediction.
Main Results:
- SIRI was identified as the strongest independent predictor of all-cause and cardiovascular mortality.
- Elevated SIRI levels significantly increased mortality risk, with consistent findings across subgroups.
- Nomograms and ML models, particularly XGBoost, demonstrated high predictive accuracy for mortality.
Conclusions:
- SIRI serves as a valuable biomarker for mortality risk stratification in CMM patients.
- Validated nomograms and prediction tools offer practical clinical utility for individualized prognosis.
- Findings support targeting systemic inflammation and nutrition for improved CMM management.
Background:
Cardiovascular multimorbidity (CMM), defined as the coexistence of multiple cardiometabolic diseases, has posed an escalating global health burden associated with premature mortality. Systemic inflammation has been increasingly recognized as a central mechanism linking cardiometabolic diseases, yet the prognostic implications of routine inflammatory and nutritional biomarkers in patients with CMM remained unclear.
Methods:
This cohort study analyzed 1,928 CMM patients from the National Health and Nutrition Examination Survey (NHANES) and 364 patients from a Chinese cohort (Gaoyou). Ten inflammatory and nutritional parameters were evaluated. Associations with all-cause and cardiovascular mortality were assessed using multivariable Cox regression and restricted cubic splines. Feature selection (SHAP, Boruta, and Lasso) was employed to identify optimal predictors, followed by construction and validation of nomogram and machine learning (ML) models.
Results:
The systemic inflammation response index (SIRI) emerged as the strongest independent predictor of mortality. Patients in the highest SIRI quartile exhibited significantly increased risks of all-cause mortality (HR = 2.34, 95% CI: 1.88-2.90) and cardiovascular mortality (HR = 2.09, 95% CI: 1.47-2.98), with consistent performance across various subgroups. Nomograms incorporating SIRI demonstrated excellent discrimination (AUCs > 0.7) and clinical utility. Among the ML models, XGBoost achieved the highest predictive efficiency at 60, 120, and 150 months.
Conclusion:
SIRI, reflecting the combined influence of inflammatory responses and nutritional status, provided an available and independent biomarker for mortality risk stratification in CMM patients. The validated nomograms and web-based prediction tool offered clinicians a practical approach for individualized prognosis and informed future strategies targeting systemic inflammation and nutrition in multimorbidity management.
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