Cardiovascular disease (CVD) remains a major global public health challenge and is closely linked to carotid atherosclerosis, a lipid-driven, multifactorial inflammatory condition (1, 2). Carotid atherosclerosis is characterized by progressive thickening of the carotid intima–media (cIMT) and the formation of atherosclerotic plaques within the arterial wall (3). Inflammation is a pivotal pathophysiological mechanism underlying atherosclerosis progression and plaque destabilization (4). The inflammatory cascade involves diverse cellular components—including neutrophils, lymphocytes, monocytes, and platelets—each contributing distinct pathological mechanisms that collectively drive vascular inflammation and endothelial dysfunction (5). However, traditional inflammatory biomarkers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are limited by their nonspecific nature and their inability to capture the complex interplay among different immune cell populations (6, 7).

Recent advances in understanding cardiovascular inflammation have led to the development of composite inflammatory indices—including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)—which integrate multiple immune cell populations to better reflect systemic inflammatory burden (8, 9). SII has been associated with coronary artery disease severity and independently predicts major adverse cardiovascular events (10, 11), while SIRI correlates with acute coronary syndrome occurrence and stroke risk in older populations (12). AISI has emerged as a predictor of adverse outcomes in cardiovascular patients (13). These indices also show associations with carotid artery stenosis severity, peripheral arterial disease, and subclinical atherosclerosis measures (14, 15), suggesting their potential as robust biomarkers for cardiovascular risk stratification in atherosclerotic disease management.

Although composite inflammatory indices have demonstrated clinical value in atherosclerotic disease assessment, current research presents several important limitations (16). Most existing studies use modest sample sizes, limiting statistical power and generalizability. Large-scale validation in Asian populations remains limited despite potential ethnic differences in inflammatory profiles (17). Additionally, systematic comparisons of different composite inflammatory indices using rigorous methodologies are lacking, and many studies employ inadequate model validation procedures (18). These limitations underscore the need for well-designed studies with appropriate sample sizes and rigorous validation.

To address these gaps, the present study investigated the association between composite inflammatory indices (SII, SIRI, and AISI) and carotid plaque formation in a large Chinese health screening population. Using systematic best subset regression analysis and rigorous internal validation, we aimed to identify the optimal combination of inflammatory and clinical parameters for predicting carotid plaque presence, thereby developing a clinically simple, feasible, and reliable predictive model for carotid atherosclerosis risk assessment.

This large-scale investigation of 9,503 Chinese health screening participants provides robust evidence establishing AISI as the superior inflammatory biomarker for carotid plaque prediction among currently available composite indices. We found that SII, SIRI, and AISI were all significantly associated with carotid plaque formation. Through rigorous best subset regression evaluating 4,095 potential model combinations, our final four-variable model—incorporating age, fasting glucose, AISI, and diabetes mellitus—achieved an AUC of 0.744 in an independent validation cohort, demonstrating strong discriminative ability. This finding not only validates the pivotal role of systemic inflammation in carotid atherosclerosis development but also offers a clinically practical tool for risk stratification in primary prevention settings.

The pathological significance of carotid plaque formation extends beyond simple arterial narrowing. Carotid atherosclerotic disease represents a progressive cerebrovascular threat, with consequences ranging from transient ischemic attacks to catastrophic stroke, wherein vascular risk escalates with increasing luminal narrowing and plaque instability (1–3). Early detection and aggressive management of modifiable risk factors are critical to preventing severe cerebrovascular complications. Recognized risk factors include traditional cardiovascular determinants—hypertension, dyslipidemia, diabetes mellitus, smoking, and advanced age—alongside emerging inflammatory biomarkers that capture the systemic inflammatory burden underlying atherosclerotic progression (22–24).

Building on this foundation, current evidence robustly supports systemic inflammation as an integral component of atherosclerotic pathogenesis, wherein persistent low-grade inflammation promotes disease progression (25). Consistent with prior investigations, the present study identified significant associations between all three composite inflammatory indices—SII, SIRI, and AISI—and carotid plaque presence. All 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). These findings suggest that individuals with elevated systemic inflammation profiles face a substantially higher risk of developing carotid plaques. While low-density lipoprotein cholesterol is a principal driver of atherosclerotic pathology, atherosclerosis is fundamentally an inflammatory disease characterized by dynamic interactions among multiple cellular populations that collectively orchestrate disease progression.

Among these indices, AISI demonstrated superior predictive performance, a finding with important mechanistic and clinical implications. In a cohort of 830 patients with type 2 diabetes, Wang et al. reported that AISI mediated 10.9% of the association between metabolic dysfunction–associated fatty liver disease and subclinical carotid atherosclerosis—exceeding SII’s mediation effect of 7.8%—suggesting AISI’s superior capacity to capture inflammatory–metabolic interactions (24). Our observed effect size for AISI (OR = 2.277, 95% CI: 2.072–2.502) is consistent with the meta-analysis by Zhao et al., which reported a pooled hazard ratio of 2.36 for SII across 15,832 patients with coronary artery disease (16). Importantly, our study establishes AISI’s superiority through direct, within-cohort comparison of all three indices using identical methodology, thereby minimizing interstudy heterogeneity and extending applicability to an asymptomatic screening population—an advance with meaningful implications for primary prevention in apparently healthy individuals.

This pathophysiological superiority of AISI derives from its unique integration of four key cellular mediators of atherosclerotic inflammation. Whereas SII captures platelet–neutrophil–lymphocyte dynamics and SIRI incorporates monocyte–neutrophil–lymphocyte interactions, AISI integrates all four cell types, providing a more complete assessment of systemic inflammatory burden. Neutrophils contribute to plaque destabilization through the release of proteolytic enzymes and reactive oxygen species, degrading extracellular matrix and amplifying local inflammation (26, 27). Monocytes, recruited through chemotactic gradients, differentiate into macrophages and foam cells, secreting proinflammatory cytokines and matrix metalloproteinases that promote plaque vulnerability (28). Platelets further amplify inflammation and precipitate thrombotic events (5, 23). Concurrently, lymphocyte depletion reflects impaired adaptive immune regulation, permitting unchecked innate immune activation. AISI’s capacity to capture these interdependent pathways—the combined dysregulation of innate immunity (elevated neutrophils, monocytes, platelets) and adaptive immune dysfunction (reduced lymphocytes)—mechanistically explains its superior predictive performance.

The clinical translation of these findings is strengthened by our methodological approach. Unlike prior investigations that employed stepwise regression or predetermined variable selection, our exhaustive evaluation of all possible variable combinations via best subset regression, combined with rigorous 10-fold cross-validation repeated five times, ensured optimal model selection while controlling for multicollinearity. The maximum variance inflation factor of 2.91 across all examined models was well below the conventional threshold of 10, confirming appropriate model specification. The model’s excellent calibration—Hosmer–Lemeshow P = 0.511, Brier score 0.198, calibration slope 0.96, and intercept 0.02—indicates that predicted probabilities closely reflect observed risks across the full spectrum, addressing a critical gap in biomarker validation often overlooked in the clinical literature (29–31). The notable stability between training and validation performance (AUC 0.738 vs. 0.744, P = 0.672) demonstrates robust generalizability and substantially reduces concerns regarding overfitting.

At the optimal probability threshold of 0.32, the model showed balanced performance with 71.5% sensitivity, 68.9% specificity, and a notably high negative predictive value of 86.5% in the independent validation cohort. This high negative predictive value provides meaningful clinical utility by substantially reducing the likelihood of advanced carotid disease in individuals below the threshold and enabling more efficient resource allocation by reducing unnecessary imaging in low-risk populations. Among model components, diabetes mellitus emerged as the strongest predictor (OR = 3.234, 95% CI: 2.727–3.836), consistent with established mechanisms whereby hyperglycemia accelerates atherosclerotic progression through advanced glycation end-product formation, enhanced oxidative stress, and amplified inflammatory signaling (32–34). The model’s simplicity—requiring only four readily available variables (age, fasting glucose, AISI from a routine complete blood count, and diabetes status)—substantially enhances its translational potential for implementation in resource-limited settings and routine primary care.

Despite these strengths, several limitations warrant consideration. The cross-sectional design precludes causal inference regarding temporal relationships between inflammatory elevation and plaque development. Single-center recruitment, while ensuring standardized protocols, may limit generalizability to populations with different genetic backgrounds or environmental exposures. Additionally, single time-point inflammatory assessment cannot capture dynamic changes that might provide additional prognostic information, and potential unmeasured confounders—including medication effects or subclinical infections—could influence inflammatory marker levels. Future prospective studies with serial AISI monitoring are needed to establish temporal relationships, evaluate whether AISI-guided interventions reduce plaque progression, and validate the model across diverse ethnic populations.

In conclusion, this study establishes AISI as the most robust composite inflammatory biomarker for carotid plaque prediction through comprehensive integration of neutrophil, monocyte, platelet, and lymphocyte dynamics. The rigorously validated four-variable prediction model offers a practical tool for risk stratification in Chinese populations, potentially enabling earlier preventive interventions and improved cardiovascular outcomes. These findings warrant external validation in diverse populations and prospective evaluation of AISI-guided strategies in clinical practice.