Systemic immune-inflammation index is associated with coronary heart disease: a cross-sectional study of NHANES

Jiwen Ma1, Ka Li1

  • 1West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China.

Insights

The systemic immune inflammation index (SII) may help predict coronary heart disease (CHD) risk. Higher SII levels were linked to lower odds of developing CHD, with gender influencing this association.

Area of Science:

  • Cardiovascular Medicine
  • Immunology
  • Biomarker Research

Background:

  • Systemic inflammation is implicated in coronary heart disease (CHD) development.
  • The systemic immune inflammation index (SII) serves as a key biomarker for systemic inflammation.
  • Understanding the relationship between SII and CHD is crucial for cardiovascular health.

Purpose of the Study:

  • To investigate the correlation between the systemic immune inflammation index (SII) and coronary heart disease (CHD).
  • To explore the potential of SII as a predictive biomarker for CHD.
  • To analyze gender-specific associations between SII and CHD.

Main Methods:

  • Multivariate logistic regression analysis was performed on a cohort of 15,905 participants.
  • Smoothing curve fitting and segmented model comparison were employed to analyze the data.
  • Adjustments were made for demographic factors including gender, age, and race.

Main Results:

  • A negative association was observed between SII and CHD after adjusting for covariates (OR 0.66; 95% CI 0.48, 0.90).
  • An inverse trend indicated that increasing SII levels correlated with decreased odds of CHD (p for trend = 0.0017).
  • Smoothing curve analysis revealed a gender-specific relationship between SII and CHD.

Conclusions:

  • Elevated SII levels may be associated with a reduced incidence of CHD, with variations observed based on gender.
  • The systemic immune inflammation index (SII) presents a potentially cost-effective and accessible tool for CHD risk assessment.
  • Further prospective research is warranted to establish causality and validate these findings in diverse populations.
Abstract

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