AUTHOR=Sun Jinxiao , Zhang Xialing , Yang Meng , Yang Shuo , Zeng Hua 

TITLE=Exploring the association between immune-inflammation index and carotid plaque formation: a cross-sectional study in a large Chinese health screening population

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1732824

DOI=10.3389/fendo.2025.1732824

ISSN=1664-2392

ABSTRACT=PurposeCardiovascular disease remains a major public health concern and is closely associated with carotid atherosclerosis, a lipid-driven inflammatory condition. Composite inflammatory indices, including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), have shown promise in cardiovascular risk assessment; however, their comparative predictive value for carotid plaque formation has not been adequately validated in large Asian populations. This study investigated the associations between these inflammatory indices and carotid plaque presence in a large-scale Chinese health screening cohort.Patients and methodsThis cross-sectional study analyzed 9,503 adults (mean age 51.6 ± 9.5 years; 50.8% male) who underwent comprehensive health examinations at Guangzhou 11th People’s Hospital between January 2018 and December 2022. Inflammatory indices were calculated from complete blood counts: SII = (neutrophils × platelets)/lymphocytes, SIRI = (neutrophils × monocytes)/lymphocytes, and AISI = (neutrophils × platelets × monocytes)/lymphocytes. Carotid plaques were identified using standardized ultrasonography according to Mannheim Consensus criteria. Best subset regression with rigorous 10-fold cross-validation identified optimal prediction models from 4,095 potential combinations. The cohort was divided into training (70%, n=6,652) and validation (30%, n=2,851) sets for model development and internal validation.ResultsCarotid plaque prevalence was 29.2%. All inflammatory indices were significantly higher in participants with plaques: SIRI (0.78 ± 0.50 vs. 0.63 ± 0.36, P<0.001), AISI (2.04 ± 1.43 vs. 1.57 ± 0.99, P<0.001), and SII (5.28 ± 2.66 vs. 4.32 ± 1.88, P<0.001). Among 89 models without multicollinearity, the optimal four-variable model included age (OR = 1.028, 95% CI: 1.020–1.036), fasting glucose (OR = 1.799, 95% CI: 1.657–1.952), AISI (OR = 2.277, 95% CI: 2.072–2.502), and diabetes mellitus (OR = 3.234, 95% CI: 2.727–3.836). This model achieved superior validation performance (AUC = 0.744) compared with models incorporating SIRI (AUC = 0.739) or traditional risk factors alone (AUC = 0.731). At the optimal threshold (0.32), the model demonstrated 71.5% sensitivity, 68.9% specificity, and 69.4% accuracy. Calibration was excellent (Hosmer–Lemeshow P = 0.511; Brier score=0.198).ConclusionAISI emerged as the most robust inflammatory biomarker for carotid plaque prediction among composite indices, suggesting its superior ability to capture the complex interplay between neutrophils, monocytes, platelets, and lymphocytes in atherosclerosis. The developed four-variable model combining AISI with traditional risk factors provides a clinically feasible tool for carotid atherosclerosis risk stratification in Chinese populations, potentially enhancing early detection and preventive interventions.