Associations of cardiometabolic index with diabetic statuses and insulin resistance: the mediating role of inflammation-related indicators.

BACKGROUND: This study aimed to analyze the associations of cardiometabolic index (CMI) with diabetic statuses and insulin resistance (IR) using data from the National Health and Nutrition Examination Survey (NHANES) and examined the potential mediating role of inflammation in these correlations. METHODS: This study enrolled 9477 participants across four NHANES cycles from 2011 to 2018. The primary outcomes of the study included the risk of having prediabetes, diabetes and the level of the homeostasis model assessment of IR (HOMA-IR). Other outcomes including the levels of fasting blood glucose (FBG), hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT) results, fasting insulin, the risk of oral hypoglycemic medicine use, insulin use, and retinopathy were also collected and analyzed. Logistic regression model, subgroup analysis, restricted cubic spine (RCS), and Pearson correlation coefficients were conducted to assess the associations of CMI with diabetic statuses and IR. The mediating role of inflammation was evaluated to investigate the potential mechanism of the associations between CMI and diabetic statuses. RESULTS: Among included participants, the CMI levels in normal participants, prediabetes and diabetes in this study were 0.48, 0.73 and 1.07. After multivariable adjustment, CMI was positively associated with the risk of prediabetes (OR = 1.49, 95% CI = 1.24-1.79), diabetes (OR = 2.14, 95% CI = 1.82-2.50) and the level of HOMA-IR (ß = 2.57, 95% CI = 2.14-3.01). Besides, an increased CMI was correlated with higher levels of FBG, HBA1c, OGTT results and fasting insulin as well as the greater risk of oral hypoglycemic medicine use and insulin use. The RCS showed an inverted L-shaped association of CMI with prediabetes and diabetes (P for non-linearity < 0.001). According to Pearson correlation coefficients, higher CMI was linked to higher rises in HOMA-IR (r = 0.224, P < 0.001). Inflammation-related indicators including leukocyte and neutrophil demonstrated significant mediating effects in the associations of CMI with prediabetes (15.5%, 9.8%), diabetes (5.1%, 6.0%) and HOMA-IR (3.3%, 2.6%). CONCLUSION: CMI was positively associated with the risk of worse diabetic statuses and higher level of IR while the associations may be partially mediated by inflammation-related indicators, suggesting that CMI could be a promising indicator for the prediction of severe diabetes and IR.

The correlation of atherogenic index of plasma with non-obstructive CAD and unfavorable prognosis among patients diagnosed with MINOCA.

BACKGROUND: The atherogenic index of plasma (AIP) is linked to lipid metabolism and has shown considerable prognostic value in cardiovascular disorders. However, its role in myocardial infarction with non-obstructive coronary arteries (MINOCA) has not been investigated. We assessed the relationship between AIP, the severity of coronary stenosis, and prognosis in MINOCA. METHODS: We included consecutive patients who were diagnosed with MINOCA. AIP was calculated using the base 10 logarithm of the ratio between the levels of TG and HDL-C. The patients were divided into four groups based on their AIP quartiles: Q1 (AIP<-0.145), Q2 (AIP≥-0.145and≤0.049), Q3 (AIP>0.049and≤0.253), and Q4 (AIP>0.253). All patients underwent follow-up for MACE. RESULTS: The final analysis included 421 patients, with 188 having normal coronaries (0 stenosis) and 233 exhibiting non-obstructive coronary artery disease (CAD) (<50 % stenosis). In the multivariate logistic analysis, highest AIP (Q4) group was significantly associated with increased risk of non-obstructive CAD in MINOCA (OR,1.994;95 % CI:1.075-3.698; P = 0.029). During the follow-up period, MACE occurred in 22.8 % of MINOCA patients. Q4 group exhibited a significantly higher rate of MACE (P = 0.021). Furthermore, when both AIP and coronary stenosis status were considered, the results revealed individuals in the Q4 group with non-obstructive CAD had the highest risk of MACE (log-rank P = 0.027). The adjusted Cox analysis indicated that the Q4 group was associated with a 2.052-fold increase in the HR of MACE. CONCLUSION: AIP exhibits a notable association with the incidence of MACE in MINOCA patients and serves as a substantial marker for non-obstructive CAD in this patient group.

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.