Association of stress hyperglycemia ratio and poor long-term prognosis in patients with myocardial infarction with non-obstructive coronary arteries.

BACKGROUND: Stress hyperglycemia ratio (SHR) is a novel biomarker of true acute hyperglycemia condition and is associated with a worse prognosis in patients with myocardial infarction (MI). However, the effects of SHR in the setting of MI with non-obstructive coronary arteries (MINOCA) have not been investigated. This study aimed to explore the association between SHR and long-term clinical outcomes among MINOCA patients. METHODS: A total of 410 MINOCA patients were included in the final analysis of this study. The patients were divided into three groups based on the SHR tertiles: [SHR1 group (SHR ≤ 0.73), (n = 143); SHR2 group (SHR 0.73-0.84), n = 131; and SHR3 group (SHR ≥ 0.84), n = 136]. Follow-up for major adverse cardiovascular events (MACE) was conducted on all patients. Cox regression and Kaplan-Meier curve analysis were used to evaluate the relationship between SHR and MACE. The receiver operating curve (ROC) analysis was applied to obtain the optimal cut-off value of SHR for predicting clinical MACE. RESULTS: A total of 92 patients developed MACE during the mean 34 months of follow-up. A significant increase in MACE was observed in the SHR3 group compared to the SHR1 and SHR2 groups (35.3% vs. 15.4% and 16.8%, respectively; P < 0.001). The Kaplan-Meier curves demonstrate that SHR3 patients had the highest MACE risk compared to SHR1 and SHR2 patients (log-rank P < 0.001). In addition, when both SHR tertiles and diabetes status were considered, those with SHR3 and diabetes had the highest hazard of MACE (log-rank P < 0.001). Multivariate Cox regression analysis showed that the SHR3 is associated with a 2.465-fold increase in the risk of MACE (adjusted HR, 2.465; 95% CI 1.461-4.159, P = 0.001). The ROC curve analysis showed that the optimal SHR cut-off value for predicting clinical MACE among MINOCA was 0.86. CONCLUSION: Our data indicates, for the first time, that SHR is independently associated with poor long-term prognosis in patients suffering from MINOCA. The optimal SHR cut-off value for predicting clinical MACE among MINOCA patients was 0.86. These findings suggest that SHR may play a potential role in the cardiovascular risk stratification of the MINOCA population.

Association between the atherogenic index of plasma and adverse long-term prognosis in patients diagnosed with chronic coronary syndrome.

BACKGROUND: The Atherogenic Index of Plasma (AIP) is a newly identified biomarker associated with lipid metabolism, demonstrating significant prognostic capabilities in individuals diagnosed with cardiovascular disease. However, its impact within the context of chronic coronary syndromes (CCS) remains unexplored. Thus, the present investigation sought to examine the potential association between AIP levels and long-term clinical outcomes in patients diagnosed with CCS. METHODS: A total of 404 patients diagnosed with CCS and who underwent coronary angiography were included in this study. The AIP index was calculated as log (triglycerides / high-density lipoprotein-cholesterol). The patients were categorized into four groups based on their AIP values: Q1 (< -0.064), Q2 (-0.064 to 0.130), Q3 (0.130 to 0.328), and Q4 (> 0.328). The occurrence of major adverse cardiovascular events (MACE) was monitored during the follow-up period for all patients. Cox regression analysis and Kaplan-Meier curve analysis were employed to examine the relationship between AIP and MACE. Furthermore, ROC analysis was utilized to determine the optimal cut-off value of AIP for predicting clinical MACE. RESULTS: During the median 35 months of follow-up, a total of 88 patients experienced MACE. Notably, the group of patients with higher AIP values (Q4 group) exhibited a significantly higher incidence of MACE compared to those with lower AIP values (Q1, Q2, and Q3 groups) (31.7% vs. 16.8%, 15.7%, and 23.0% respectively; P = 0.023). The Kaplan-Meier curves illustrated those patients in the Q4 group had the highest risk of MACE relative to patients in the other groups (log-rank P = 0.014). Furthermore, the multivariate Cox regression analysis demonstrated that individuals in the Q4 group had a 7.892-fold increased risk of MACE compared to those in the Q1 group (adjusted HR, 7.892; 95% CI 1.818-34.269; P = 0.006). Additionally, the ROC curve analysis revealed an optimal AIP cut-off value of 0.24 for predicting clinical MACE in patients with CCS. CONCLUSION: Our data indicate, for the first time, that AIP is independently associated with poor long-term prognosis in patients suffering from CCS. The optimal AIP cut-off value for predicting clinical MACE among CCS patients was 0.24.