# The Association of Atherogenic Indices with Coronary Slow Flow: Evidence from a Large Cohort Study

## TL;DR

This study found that a specific atherogenic index, AIP, is linked to coronary slow flow, a condition involving delayed blood flow in the heart's small vessels.

## Contribution

The study identifies AIP as an independent predictor of coronary slow flow in a large real-world cohort.

## Key findings

- Patients with coronary slow flow had higher AIP, AC, non-HDL-C, and CRI indices compared to controls.
- AIP was an independent predictor of coronary slow flow with a modest discriminatory capacity.
- AIP showed a weak but significant correlation with microvascular dysfunction in coronary slow flow patients.

## Abstract

Background: Coronary slow flow (CSF) is a microvascular disorder characterized by delayed perfusion despite the absence of significant epicardial stenosis. Although its exact pathophysiology remains unclear, endothelial dysfunction, oxidative stress, and atherogenic dyslipidemia have been implicated. Traditional lipid parameters may not fully capture the atherogenic burden, whereas atherogenic indices such as the atherogenic index of plasma (AIP), atherogenic coefficient (AC), and Castelli risk indices (CRI-I and CRI-II) may provide better predictive value. This study aimed to investigate the association between atherogenic indices and CSF in a large real-world angiographic cohort. Methods: This retrospective study included 25,486 patients who underwent coronary angiography between September 2020 and June 2024. A total of 464 patients with CSF (diagnosed by TIMI frame count criteria) and 408 controls with normal coronary flow (NCF) were identified. Atherogenic indices, including AIP, AC, CRI-I, CRI-II, and non-HDL cholesterol (non-HDL-C), were calculated. Multivariate logistic regression analysis identified independent predictors of CSF, while receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of each lipid-related parameter. Results: Patients with CSF had significantly higher AIP, AC, non-HDL-C, and CRI indices and lower HDL-C levels compared to controls (all, p < 0.05). Multivariate analysis identified AIP (OR: 1.73, 95% CI: 1.18–2.44, p = 0.004), age (OR: 1.02, 95% CI: 1.01–1.06, p = 0.014) and smoking (OR: 2.22, 95% CI: 1.36–2.84, p = 0.003) as independent predictors of CSF. ROC analysis showed modest but statistically significant discriminatory capacity for AIP (cut-off: 0.50; AUC: 0.629; 95% CI: 0.591–0.667; p < 0.001). AIP also demonstrated a weak yet significant correlation with mean TIMI frame count (rho = 0.245, p < 0.001), suggesting a potential link to microvascular dysfunction. Conclusions: Among the evaluated atherogenic indices, only AIP demonstrated an independent association with CSF. Despite modest discriminative performance that does not support standalone clinical prediction, AIP may reflect an underlying metabolic phenotype associated with CSF and serve as a complementary marker alongside traditional risk assessment. These findings should be interpreted as hypothesis-generating and warrant prospective validation.