Contrast-induced nephropathy (CIN) is one of the most frequent complications following percutaneous coronary intervention (PCI) (1, 2). CIN not only prolongs hospital stays but can also lead to increased medical expenses, an elevated risk of renal replacement therapy, recurrent vascular reconstruction procedures, and increased long-term mortality (3, 4). Therefore, identifying risk factors for CIN and implementing effective preventive measures are of paramount importance.

Insulin resistance (IR) constitutes a crucial pathogenic mechanism in diabetes mellitus (DM) and significantly influences both cardiovascular disease incidence and patient prognosis, while also being closely associated with the prevalence of chronic kidney dysfunction (5–7). The triglyceride-glucose (TyG) index is a novel and easily accessible biomarker for IR (8). Prior studies have suggested a potential association between an elevated TyG index and CIN development in specific populations, such as patients with ST-elevation myocardial infarction (STEMI) (5, 9, 10).

Furthermore, inflammation is recognized as one of the primary pathophysiological mechanisms underlying CIN (11). High-sensitivity C-reactive protein (hs-CRP) serves as an important biomarker of systemic inflammatory response (5). Previous studies have established an association between elevated hs-CRP levels and post-PCI CIN, identifying it as an independent risk factor (1, 12). However, current research has primarily focused on STEMI patient populations, with limited evidence regarding whether these conclusions can be extended to acute coronary syndrome (ACS) patients, and the presence of interactive effects remains unclear (4, 5).

This study aims to investigate the individual and combined associations of the TyG index and hs-CRP with CIN in ACS patients undergoing PCI. By analyzing their independent associations, potential interactive effects, and exploring optimal thresholds for risk stratification, we seek to provide an integrated biomarker approach to help identify high-risk patients and guide targeted preventive strategies.

A total of 1,818 patients were included in this study, of whom 296 developed CIN, while 1,522 did not develop CIN. Baseline data showed that patients in the CIN group had significantly higher BMI, heart rate, peak hs-CRP, total cholesterol, TG, low-density lipoprotein cholesterol (LDL-C), FPG, and calcium channel blocker (CCB) treatment usage rates, while eGFR levels were significantly lower (p < 0.05). Furthermore, analysis by ACS subtype revealed that the incidence of CIN was significantly higher in STEMI patients (58%) than in those with NSTEMI (26%) or unstable angina (UA) (17%) (p < 0.05). Detailed data are shown in Table 1.

Univariate and multivariate regression analyses showed that BMI (1.17, 95% CI 1.12–1.23, p < 0.001), heart rate (1.02, 95% CI 1.00–1.04, p = 0.011), peak hs-CRP (1.07, 95% CI 1.05–1.10, p < 0.001), total cholesterol (1.46, 95% CI 1.22–1.75, p < 0.001), LDL-C (1.61, 95% CI 1.31–1.99, p < 0.001), and the TyG index (4.08, 95% CI 2.95–5.71, p < 0.001) were all independent risk factors (see Table 2).

The multivariable logistic regression model incorporating both statistically significant predictors and clinically mandated variables demonstrated good discriminatory ability, with an area under the ROC curve (AUC) of 0.780 (95% CI 0.750–0.809). At the optimal cutoff point of 0.163, the model achieved a sensitivity of 77.7% and a specificity of 69.3% (Figure 2A). After bootstrap internal validation, the optimism-corrected AUC was 0.772 (95% CI 0.744–0.801) with an optimism of 0.008, indicating minimal overfitting (Figure 2B). Additionally, variance inflation factor (VIF) collinearity testing showed all variables had VIF values <4, indicating no collinearity issues (Supplementary Table 1).

RCS analysis revealed significant nonlinear relationships between the TyG index and peak hs-CRP with CIN occurrence (P for nonlinear < 0.001). Threshold effect analysis of the TyG index and peak hs-CRP showed significant correlations for both (Figure 3).

Overall analysis results indicated a significant association between the TyG index and CIN occurrence (OR = 3.861, 95% CI 2.825–5.346, p < 0.001). Through threshold analysis (Table 3), the optimal cutoff point for the TyG index was 8.5. When the TyG index was <8.5, its effect on CIN was weak (OR = 1.171, 95% CI 0.581–2.813, p = 0.690); when the TyG index was >8.5, its effect on CIN was significantly enhanced (OR = 5.117, 95% CI 3.527–7.473, p < 0.001).

Peak hs-CRP overall analysis also showed significant association (OR = 3.009, 95% CI 2.335–3.913, p < 0.001), with an optimal cutoff point of 5.0 mg/L. When peak hs-CRP was <5.0 mg/L, it was associated with increased CIN risk (OR = 2.248, 95% CI 1.417–3.746, p = 0.001); when peak hs-CRP was >5.0 mg/L, the risk was further increased (OR = 3.599, 95% CI 2.490–5.242, p < 0.001).

This study identified multiple independent risk factors for CIN development following PCI through univariate and multivariate regression analyses, including BMI, heart rate, peak hs-CRP, total cholesterol, LDL-C, and the TyG index. These results indicate that CIN occurrence stems from multiple factors involving metabolic, inflammatory, and hemodynamic aspects. This study identified relevant thresholds for the TyG index and hs-CRP in predicting CIN following PCI in ACS patients using restricted cubic spline (RCS) and threshold effect analyses, thereby contributing to the refinement of clinical risk stratification. Additionally, a significant additive interaction exists between the TyG index and hs-CRP, suggesting synergistic effects of metabolic and inflammatory factors in CIN development. These findings provide important evidence for understanding CIN pathogenesis and developing prevention strategies.

Previous studies have extensively demonstrated that IR adversely affects kidney function and prognosis, also increasing CIN risk following elective PCI (6, 24, 25). The TyG index, as a novel IR biomarker in recent years, offers advantages of easy accessibility, low cost, and simple operation. Previous research has already revealed the negative impact of an elevated TyG index on kidney function (26). Aktas H demonstrated that in non-diabetic non-STEMI patients, a high TyG index similarly increases post-procedural CIN incidence (8). Similarly, Gursoy E found that a high TyG index was associated with increased CIN risk in patients with similar inclusion criteria (20). This indicates that the TyG index is a risk factor for CIN (27). Consistent with previous studies, our research confirms the importance of the TyG index and hs-CRP as CIN risk factors. Similarly, hs-CRP as a systemic inflammation indicator has been found to be a predictor of post-PCI CIN, especially in patient populations with high inflammatory levels (11), which is consistent with this study. Threshold analysis in this study identified two cut-off values with clear clinical applicability: a TyG index ≥ 8.5 and hs-CRP ≥ 5 mg/L. Patients meeting both criteria exhibited a further increased risk of CIN compared with those with elevation of either marker alone, suggesting a synergistic effect of metabolic dysfunction and inflammatory status in the development of CIN. These findings underscore the need for heightened clinical vigilance in this patient subgroup.

The mechanisms by which the TyG index and hs-CRP contribute to the development of CIN after PCI in patients with ACS have not yet been fully elucidated. As a representative surrogate marker of IR, the TyG index has been clearly shown to increase the risk of cardiovascular disease and DM (28, 29). Evidence indicates that a hyperglycemic milieu promotes oxidative stress through multiple pathways, including the formation of advanced glycation end products, activation of protein kinase C, mitochondrial dysfunction, and stimulation of the polyol pathway. These processes enhance the generation of reactive oxygen species, leading to endothelial dysfunction, vascular inflammation, and aggravated cardiovascular injury. Moreover, oxidative stress disrupts nitric oxide signaling, impairs vasodilation, and promotes vasoconstriction. Oxidative stress and inflammation can directly induce microcirculatory dysfunction caused by atherosclerotic plaque formation and thrombosis (30, 31). In addition, IR exerts deleterious effects on renal function by increasing glomerular hydrostatic pressure, enhancing glomerular permeability, and ultimately resulting in glomerular hyperfiltration (32–34). The role of hs-CRP in the development of CIN may involve multiple mechanisms. Previous studies have demonstrated a close association between glucose metabolism and inflammation (35–37). Reports suggest that this process may be related to the production of renal inflammatory cytokines and chemokines, upregulation of leukocyte adhesion molecules, and infiltration of various inflammatory cells into renal tissue (38). The interaction between these two mechanisms is likely attributable to the mutual reinforcement between disordered glucose metabolism (such as IR) and chronic low-grade inflammation (as reflected by elevated hs-CRP levels), forming a vicious cycle. IR adversely affects renal function, whereas inflammatory mediators—such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)—further impair insulin signaling pathways, thereby exacerbating IR. This metabolic–inflammatory synergy may jointly damage the renal microcirculation, increase oxidative stress, and reduce renal tolerance to contrast media toxicity, ultimately contributing to an increased risk of CIN (39, 40).

Notably, in the baseline analysis of this study, the median contrast volume was slightly lower in patients who developed CIN than in those who did not (P = 0.07), which appears inconsistent with the established role of contrast volume as a classic risk factor. We believe this finding most likely reflects risk-adaptive management in real-world practice. Operators make procedural decisions based on patients’ real-time clinical information, among which baseline renal function (eGFR) is a key determinant. In our cohort, baseline eGFR was significantly lower in the CIN group (P = 0.035). Consequently, operators may have consciously adopted stricter contrast minimization strategies in patients identified as having poorer baseline renal function and higher risk. This risk-based, differential treatment may have introduced a “confounding by treatment” effect, leading to an apparently lower contrast volume in the high-risk group. This interpretation is supported by the multivariable analysis, in which contrast volume did not demonstrate independent predictive value after adjustment for confounders, including BMI and total cholesterol.

The strengths of this study are twofold. First, this cohort study, based on a relatively large sample size, identified a synergistic effect of the TyG index and hs-CRP on the risk of CIN in patients with ACS undergoing PCI, with the robustness of the findings supported by internal validation. Second, clinically relevant risk thresholds for both indicators were identified, which may facilitate improved clinical risk stratification. Several limitations should also be acknowledged. First, this was a single-center study predominantly involving an Asian population, and patients with impaired renal function were excluded, which may limit the generalizability of the findings. Second, due to the retrospective design, causal inferences cannot be established and require confirmation in prospective studies. Third, the TyG index and hs-CRP were measured only once before PCI, precluding evaluation of their dynamic changes over time. Fourth, despite multivariable adjustment, residual confounding from unmeasured factors such as lifestyle and dietary habits cannot be excluded. Fifth, inter-operator variability in coronary angiography and PCI procedures may have influenced outcomes. Sixth, interaction analyses resulted in small sample sizes and limited event numbers in some subgroups, potentially leading to unstable estimates. Finally, in emergency settings of acute myocardial infarction, standardized fasting blood sampling was not feasible in some patients, which may have affected laboratory parameters sensitive to feeding status.

In conclusion, this study confirmed that BMI, heart rate, hs-CRP, cholesterol parameters, and the TyG index are independent risk factors for post-PCI CIN. The nonlinear and additive interactions between the TyG index and hs-CRP highlight the importance of metabolic-inflammatory interactions. These findings provide theoretical support for optimizing CIN risk prediction and developing personalized prevention strategies, opening new directions for future research and clinical practice.