Association between the atherogenic index of plasma and coronary collateral circulation in patients with chronic total occlusion.

BACKGROUND: The atherogenic index of plasma (AIP) is considered an independent risk factor for coronary artery disease (CAD). The present study investigated whether AIP correlates with the formation of coronary collateral circulation (CCC) in CAD patients with chronic total occlusion (CTO). METHODS: This retrospective study included 1093 CAD patients with CTO confirmed by coronary angiography from January 2020 to December 2020 at Beijing Anzhen Hospital. Based on the Rentrop scoring system, the patients were divided into the good CCC group and the poor CCC group. AIP was calculated by log (triglyceride/high-density lipoprotein cholesterol). Meanwhile, the study population was further divided into four groups according to the quartiles of AIP. RESULTS: Patients in the poor CCC group exhibited significantly higher AIP compared to those in the good CCC group (0.31 ± 0.27 vs. 0.14 ± 0.24, p < 0.001). Multivariate logistic regression analysis revealed an independent association between AIP and poor CCC, regardless of whether AIP was treated as a continuous or categorical variable (p < 0.001), after adjusting for confounding factors. Besides, this association remained consistent across most subgroups. The incorporation of AIP into the baseline model significantly enhanced the accuracy of identifying poor CCC [area under the curve (AUC): baseline model, 0.661 vs. baseline model + AIP, 0.721, p for comparison < 0.001]. CONCLUSIONS: Elevated AIP is independently associated with an increased risk of poor CCC in CAD patients with CTO, and AIP may improve the ability to identify poor CCC in clinical practice.

Nonlinear association between atherogenic index of plasma and type 2 diabetes mellitus in overweight and obesity patients: evidence from Chinese medical examination data.

BACKGROUND: The atherogenic index of plasma (AIP) is closely associated with the onset of diabetes, with obesity being a significant risk factor for type 2 diabetes mellitus (T2DM). However, the association between the AIP and T2DM in overweight and obese populations has been infrequently studied. Therefore, this study aimed to explore this association in overweight and obese individuals with T2DM. METHODS: This cross-sectional analysis utilized data from 40,633 participants with a body mass index (BMI) ≥ 24 kg/m2 who were screened from January 2018 to December 2023 at Henan Provincial People's Hospital. Participants were categorized into groups of overweight and obese individuals with and without diabetes according to the T2DM criteria. The AIP, our dependent variable, was calculated using the formula log10 [(TG mol/L)/HDL-C (mol/L)]. We investigated the association between the AIP and T2DM in overweight and obese individuals using multivariate logistic regression, subgroup analysis, generalized additive models, smoothed curve fitting, and threshold effect analysis. Additionally, mediation analysis evaluated the role of inflammatory cells in AIP-related T2DM. RESULTS: Overweight and obese patients with T2DM exhibited higher AIP levels than those without diabetes. After adjusting for confounders, our results indicated a significant association between the AIP and the risk of T2DM in overweight and obese individuals (odds ratio (OR) = 5.17, 95% confidence interval (CI) 4.69-5.69). Notably, participants with a high baseline AIP (Q4 group) had a significantly greater risk of T2DM than those in the Q1 group, with an OR of 3.18 (95% CI 2.94-3.45). Subgroup analysis revealed that the association between the AIP and T2DM decreased with increasing age (interaction P < 0.001). In overweight and obese populations, the association between AIP and T2DM risk displayed a J-shaped nonlinear pattern, with AIP > - 0.07 indicating a significant increase in T2DM risk. Various inflammatory cells, including neutrophils, leukocytes, and monocytes, mediated 4.66%, 4.16%, and 1.93% of the associations, respectively. CONCLUSION: In overweight and obese individuals, the AIP was independently associated with T2DM, exhibiting a nonlinear association. Additionally, the association between the AIP and T2DM decreased with advancing age. Multiple types of inflammatory cells mediate this association.

Association between atherogenic index of plasma and gestational diabetes mellitus: a prospective cohort study based on the Korean population.

BACKGROUND: Atherogenic index of plasma (AIP) is a non-traditional lipid parameter that can reflect the burden of atherosclerosis. A lipid profile resembling atherosclerosis emerged during pregnancy. Although lipid metabolism is pivotal in diabetes pathogenesis, there is no evidence linking AIP to gestational diabetes mellitus (GDM). Therefore, our objective was to explore the relationship between AIP and GDM and assess AIP's predictive capability for GDM. METHODS: This was a secondary analysis based on data from a prospective cohort study in Korea involving 585 single pregnant women. AIP was calculated as log10 (TG/HDL). We examined the relationship between AIP and GDM using logistic regression models, curve fitting, sensitivity analyses, and subgroup analyses. Receiver operating characteristic (ROC) analysis was also used to determine the ability of AIP to predict GDM. RESULTS: The average age of the participants was 32.06 ± 3.76 years. The AIP was 0.24 ± 0.20 on average. The GDM incidence was 6.15%. After adjustment for potentially confounding variables, AIP showed a positive linear relationship with GDM (P for non-linearity: 0.801, OR 1.58, 95% CI 1.27-1.97). The robustness of the connection between AIP and GDM was demonstrated by sensitivity analyses and subgroup analyses. An area under the ROC curve of 0.7879 (95% CI 0.7087-0.8671) indicates that AIP is an excellent predictor of GDM. With a specificity of 75.41% and sensitivity of 72.22%, the ideal AIP cut-off value for identifying GDM was 0.3557. CONCLUSIONS: This study revealed that the AIP at 10-14 weeks of gestation was independently and positively correlated with GDM risk. AIP could serve as an early screening and monitoring tool for pregnant women at high risk of GDM, thereby optimizing GDM prevention strategies. TRIAL REGISTRATION: ClinicalTrials.gov registration no. NCT02276144.

The Predictive Value of Atherogenic Index of Plasma, Non- High Density Lipoprotein Cholesterol (Non-HDL-C), Non-HDL-C/HDL-C, and Lipoprotein Combine Index for Stroke Incidence and Prognosis in Maintenance Hemodialysis Patients.

Purpose: The serum lipid level is strongly associated with atherosclerosis. However, research on the relationship between lipid-derived indices and acute ischemic stroke (AIS) occurrence in hemodialysis populations is limited. This study aimed to explore the predictive value of lipid-derived indices, including atherogenic index of plasma (AIP), Non- high density lipoprotein cholesterol (Non-HDL-C), Non-HDL-C/HDL-C, and lipoprotein combine index (LCI) in clinical practice for the occurrence and prognosis of AIS in hemodialysis patients. Methods: A total of 451 patients undergoing maintenance hemodialysis were screened and 350 were enrolled in this study. The lipid parameters exhibit a progressive increase across the tertiles, with values rising from Q1 through Q3. Enrolled patients were divided into three groups (Q1, Q2, and Q3) based on tertiles of AIP, Non-HDL-C, Non-HDL-C/HDL-C, and LCI values. Kaplan-Meier curves were performed to investigate the association between the AIP, Non-HDL-C, Non-HDL-C/HDL-C, LCI and AIS-free survival in hemodialysis patients. Chi-square analysis was used to explore the association between the AIP, Non-HDL-C, Non-HDL-C/HDL-C, LCI and AIS outcomes in hemodialysis patients. AIS outcomes were assessed using the modified Rankin Scale (mRS). Results: Kaplan-Meier analysis revealed that the AIS-free survival rates were significantly higher in the Q1 group compared to Q2 and Q3 groups for AIP, Non-HDL-C, Non-HDL-C/HDL-C, and LCI. Log rank tests showed statistically significant differences between the Q1 group and the Q2 and Q3 groups (p < 0.05 for all). The proportion of patients with a good outcome mRS was higher in the Q1 group compared to the Q2-Q3 groups (AIP: 0.818 vs 0.792; Non- HDL-C: 0.866 vs 0.767; Non- HDL-C/HDL-C: 0.867 vs 0.767; LCI: 0.938 vs 0.750). Conclusion: The four lipid-derived parameters are effective predictors of AIS in patients undergoing hemodialysis, and AIP has a strongest correlation with the risk of AIS. Hemodialysis patients with elevated levels of the four lipid-derived indices had a higher incidence of AIS and poorer functional outcomes compared to those with lower levels. Our conclusions may require confirmation by further research in the future.

Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS.

BACKGROUND: Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism status, remains ambiguous. METHODS: A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1 (AIP ≤ 0.122), Q2 (0.122 < AIP ≤ 0.329), Q3 (0.329 < AIP ≤ 0.562), and Q4 (AIP > 0.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan-Meier curves, multivariate Cox proportional hazard models, and Restricted cubic spline analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states. RESULTS: During an average follow-up of 8.72 years, 734 participants (8.4%) had a first stroke event. The risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan-Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P values < 0.05). After adjusting for potential confounders, the risk of stroke was significantly higher in the Q2, Q3, and Q4 groups than in the Q1 group in all participants. The respective hazard ratios (95% confidence interval) for stroke in the Q2, Q3, and Q4 groups were 1.34 (1.05-1.71), 1.52 (1.19-1.93), and 1.84 (1.45-2.34). Furthermore, high levels of AIP were found to be linked to an increased risk of stroke in both pre-diabetic and diabetic participants across all three Cox models. However, this association was not observed in participants with normal glucose regulation (NGR) (p > 0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with higher hazard ratios for stroke in all participants and those with glucose metabolism disorders. CONCLUSIONS: An increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual's glucose metabolism status.

Atherogenic Index of Plasma as an Early Marker of Chronic Kidney Disease and Liver Injury in Type 2 Diabetes.

Background: Diabetic kidney disease (DKD) is the main cause of end-stage renal disease and has a high mortality rate. Currently, no effective treatments are available to reduce the progression of kidney damage associated with diabetes. Objectives: To explore the influence and predictive value of the atherogenic index of plasma (AIP) on early chronic kidney disease and liver injury in patients with type 2 diabetes mellitus (T2DM). Methods: Medical records of 1057 hospitalized adult patients with T2DM between January 2021 and December 2022 were collected. The predictive value of AIP, renal function, and liver injury in patients with T2DM were analyzed using Pearson's correlation, multiple logistic regression, and receiver operating characteristic (ROC) curve analyses. Results: AIP was a sensitive indicator of early liver and kidney injury in patients with T2DM. Patients in the DKD group showed increased AIP that positively correlated with serum creatinine, uric acid, and ß2-microglobulin levels. Increased AIP negatively correlated with estimated glomerular filtration rate (eGFR). AIP significantly correlated with alanine aminotransferase and aspartate aminotransferase levels and glutamyl transpeptidase-to-platelet ratio (GPR). An eGFR of 60-100 mL/min/1.73 m2 significantly increased the risk of DKD as the AIP increased. At lower GPR levels, the risk of DKD significantly increased with increasing AIP. However, no significant difference was found between the 2 groups when the GPR was >0.1407. The ROC curve analysis showed that AIP could predict early liver injury. Conclusions: AIP is directly involved in early liver and kidney injury in T2DM and may be a sensitive indicator for early detection.

Diabetes and its complications are a global public health concern. Diabetic kidney disease (DKD) is the main cause of end-stage renal disease, and metabolism-related disease factors are found throughout the progression of DKD. This study identified common sensitive indicators of early metabolism-related damage to liver and kidney function in patients with T2DM.

Association between the atherogenic index of plasma and bone mineral density among adult women: NHANES (2011-2018).

Background: Studies on the relationship between the atherogenic index of plasma (AIP) and bone mineral density (BMD) among adult women in the United States are limited. The purpose of this study was to explore this association using a sizable, nationally representative sample. Methods: Data from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES) were used in this observational study. The AIP was computed as log10 (triglycerides/high-density lipoprotein cholesterol). Total BMD was measured via dual-energy X-ray densitometry. We constructed multiple linear regression models to evaluate the correlation between the AIP and BMD. The non-linear relationship was characterized by smooth curve fitting and generalized additive models. We also conducted subgroup and interaction analyses. Results: In this study, we included 2,362 adult women with a mean age of 38.13 ± 12.42 years. The results of multiple linear regression analysis, the AIP and total BMD showed a negative association (ß = -0.021, 95%CI: -0.037, -0.006). The curve fitting analysis and threshold effect analysis showed a non-linear relationship between the two variables, and the inflection point of the AIP was found to be -0.61. The total BMD decreased significantly when the AIP reached this value (ß = -0.03, 95%CI: -0.04, -0.01). The results of the subgroup analysis showed that AIP and total BMD had a strong negative relationship in participants who were below 45 years old (ß = -0.023; 95% CI: -0.041, -0.004), overweight (BMI ≥ 25 kg/m2) (ß = -0.022; 95% CI: -0.041, -0.002), had a higher education level (ß = -0.025; 95% CI: -0.044, -0.006), and had no partners (ß = -0.014; 95% CI: -0.06, -0.009). Conclusions: We found a negative correlation between the AIP and total BMD. Clinicians should pay attention to patients with high AIP, which might indicate a low BMD and has reference significance in preventing osteoporosis.

Atherogenic index of plasma: a new indicator for assessing the short-term mortality of patients with acute decompensated heart failure.

Objective: Arteriosclerosis is a primary causative factor in cardiovascular diseases. This study aims to explore the correlation between the atherogenic index of plasma (AIP) and the 30-day mortality rate in patients with acute decompensated heart failure (ADHF). Methods: A total of 1,248 ADHF patients recruited from the Jiangxi-Acute Decompensated Heart Failure1 (JX-ADHF1) cohort between 2019 and 2022 were selected for this study. The primary outcome was the 30-day mortality rate. Multivariable Cox regression, restricted cubic splines (RCS), and stratified analyses were utilized to assess the relationship between AIP and the 30-day mortality rate in ADHF patients. Mediation models were employed for exploratory analysis of the roles of inflammation, oxidative stress, and nutrition in the association between AIP and the 30-day mortality rate in ADHF patients. Results: During the 30-day follow-up, 42 (3.37%) of the ADHF patients died. The mortality rates corresponding to the quartiles of AIP were as follows: Q1: 1.28%, Q2: 2.88%, Q3: 2.88%, Q4: 6.41%. The multivariable Cox regression revealed a positive correlation between high AIP and the 30-day mortality rate in ADHF patients [Hazard ratio (HR) 3.94, 95% confidence interval (CI): 1.08-14.28], independent of age, gender, heart failure type, cardiac function classification, and comorbidities. It is important to note that there was a U-shaped curve association between AIP (<0.24) and the 30-day mortality rate before the fourth quartile, with the lowest 30-day mortality risk in ADHF patients around an AIP of -0.1. Furthermore, mediation analysis suggested significant mediating effects of inflammation and nutrition on the 30-day mortality rate in ADHF patients related to AIP, with inflammation accounting for approximately 24.29% and nutrition for about 8.16% of the mediation effect. Conclusion: This retrospective cohort analysis reveals for the first time the association between AIP and the 30-day mortality rate in ADHF patients. According to our findings, maintaining an AIP around -0.1 in ADHF patients could be crucial for improving poor prognoses from a medical perspective. Additionally, for ADHF patients with high AIP, it is important to assess and, if necessary, enhance nutritional support and anti-inflammatory treatment.

Roles of Nontraditional Lipid Parameters for Predicting Restenosis in Patients with Intracranial Atherosclerotic Stenosis After Endovascular Treatment.

PURPOSE: Nontraditional lipid parameters are associated with intracranial atherosclerotic stenosis (ICAS) progression. This study aimed to investigate the association of nontraditional lipid parameters with the risk of restenosis in patients with ICAS after endovascular treatment (EVT). METHODS: This study retrospectively enrolled consecutive patients with symptomatic ICAS after successful EVT followed by at least 3 months of angiography. Participants were divided into restenosis or non-restenosis groups based on the angiographic follow-up results. The nontraditional lipid parameters were calculated from conventional lipid parameters. The COX regression models and restricted cubic splines (RCS) were used to explore the association between nontraditional lipid parameters and restenosis. RESULTS: This study recruited 222 cases with 224 lesions eligible for our study, of which 56 (25%) had restenosis. Compared with the non-restenosis group, patients in the restenosis group had higher levels of the atherogenic index of plasma (AIP) (0.211, interquartile range, IQR, 0.065-0.404 vs. 0.083, IQR, -0.052-0.265, P = 0.001), remnant cholesterol (RC) (0.55, IQR, 0.33-0.77 vs. 0.30, IQR, 0.18-0.49, P < 0.001) and Castelli's index­I (CRI-I) (4.13, IQR, 3.39-5.34 vs. 3.74, IQR, 2.94-4.81, P = 0.030). In the multivariable COX regression analysis, a 0.1 unit increase of AIP was an independent risk factor for restenosis (hazard ratio, HR = 1.20, 95% confidence interval, CI 1.05-1.35, P = 0.005) whereas such an association was not observed for RC (HR = 1.01, 95% CI 0.90-1.15, P = 0.835). The restricted cubic spline (RCS) plot revealed a linear relationship between AIP and restenosis (P for nonlinear = 0.835) but a nonlinear relationship for RC (P for nonlinear = 0.012). Patients were stratified according to tertiles (T) of AIP and RC and the risk of restenosis increased in T3 compared to T1 (HR = 3.21, 95% CI 1.35-7.62, P = 0.008 and HR = 2.99, 95% CI 1.11-8.03, P = 0.030, respectively). Furthermore, this association remained stable within each LDL­C level subgroup. CONCLUSION: The AIP and RC were positively and independently associated with restenosis in patients with ICAS after EVT.

Non-linear association of atherogenic index of plasma with bone mineral density a cross-sectional study.

INTRODUCTION: Although there has been abundant evidence of the association between dyslipidemia as a single factor and osteoporosis, the non-linear relationship between osteoporosis and the Atherogenic Index of Plasma (AIP) has not yet been thoroughly investigated. This study aimed to investigate the complex relationship between AIP and bone mineral density (BMD) to elucidate their interrelationship. METHODS: An analysis of 2007-2018 National Health and Nutrition Survey (NHANES) data was conducted for this study. The study enrolled 5,019 participants. Logarithmically multiplying triglycerides and high-density lipoprotein cholesterol yields the AIP (base 10). The measured variables consisted of BMD in the total femur (TF), femoral neck (FN), and lumbar spine (LS). The association between AIP and BMD was examined using a range of statistical models, such as weighted multivariable logistic regression, generalized additive model, etc. RESULTS: It was found that AIP was positively associated with BMD after adjusting for age, gender, race, socioeconomic status, degree of education, income, Consuming alcoholic beverages, osteoporosis status (Yes or No), ALT, AST, serum creatinine, and total calcium levels. Further studies supported the association link between elevated BMD and AIP. Furthermore, compared to men, females had a higher positive connection between AIP and BMD. In general, there was a curve in the reverse L-shape seen, with a point of change around 0.877, indicating a relationship between AIP and TF BMD. Moreover, a curve exhibiting an L-formed pattern, with a point of inflection at around 0.702, was seen between AIP and FN BMD. In addition, a J-shaped curve was seen, with a point of inflection at 0.092, which demonstrates the association between AIP and LS BMD. CONCLUSION: The AIP and TF BMD curves resemble inverted L shapes, as do the AIP and FN BMD curves. The relationship between AIP and LS BMD was further demonstrated by a J-shaped curve. The results indicate a possible association between AIP and bone mineral density, which should be explored in more detail.

Association between the atherogenic index of plasma and left ventricular hypertrophy in patients with obstructive sleep apnea: a retrospective cross-sectional study.

BACKGROUND: The atherogenic index of plasma (AIP) is a simple and reliable marker of insulin resistance and is closely associated with various cardiovascular diseases (CVDs). However, the relationships between AIP and left ventricular (LV) geometric indicators have not been adequately assessed. This study was carried out to investigate the association between AIP and LV geometric abnormalities in obstructive sleep apnea (OSA) patients. METHODS: This retrospective cross-sectional study included a total of 618 OSA patients (57.3 ± 12.4 years, 73.1% males, BMI 28.1 ± 4.2 kg/m2) who underwent echocardiography. Patients with OSA were diagnosed with clinical symptoms and an apnea-hypopnea index ≥ 5.0. LV hypertrophy (LVH) was defined as left ventricular mass index (LVMIh2.7) ≥ 50.0 g/m2.7 for men and 47.0 g/m2.7 for women. AIP was calculated as log10 (TG/HDL-C). RESULTS: Compared with the non-LVH group, AIP was significantly higher in the LVH group (0.19 ± 0.29 vs 0.24 ± 0.28, P = 0.024) and the concentric LVH group (0.18 ± 0.29, 0.19 ± 0.30, 0.20 ± 0.26 and 0.29 ± 0.29 in the control, concentric remodeling, eccentric hypertrophy and concentric hypertrophy groups, respectively, P = 0.021). Meanwhile, in the group of patients with the highest AIP tertile, the levels of LVMIh2.7 (42.8 ± 10.5, 43.2 ± 9.3 and 46.1 ± 12.1 in the T1, T2 and T3 groups, respectively, P = 0.003), and the prevalence of LVH (25.2%, 24.0% and 34.6% in the T1, T2 and T3 groups, respectively, P = 0.032) and concentric LVH (10.7%, 9.8% and 20.2% in the T1, T2 and T3 groups, respectively, P = 0.053) were higher compared with those in the other groups. Positive correlations between AIP and LV geometric indicators including the LVMIh2.7, LVMIBSA, LV mass (LVM), diastolic left ventricular inner diameter (LVIDd), diastolic left ventricular posterior wall thickness (PWTd) and diastolic interventricular septal thickness (IVSTd), were revealed according to correlation analysis (P < 0.05). Furthermore, AIP was independently associated with LVMIh2.7 according to multivariate linear regression model (ß = 0.125, P = 0.001). Notably, AIP remained independently associated with an elevated risk of LVH [odds ratio (OR) = 1.317 per 1 standard deviation (SD) increment, 95% confidence interval (CI): 1.058 - 1.639, P = 0.014) and concentric LVH (OR = 1.545 per 1 SD increment, 95% CI: 1.173 - 2.035, P = 0.002) after fully adjusting for all confounding risk factors by multivariate logistic regression analyses. CONCLUSIONS: AIP was independently associated with an increased risk of LVH and concentric LVH in OSA patients. Therefore, AIP, as a practical and cost-effective test, might be useful in monitoring hypertrophic remodeling of the heart and improving CVDs risk stratification in clinical management of OSA.

Predicting coronary artery severity in patients undergoing coronary computed tomographic angiography: Insights from pan-immune inflammation value and atherogenic index of plasma.

BACKGROUND AND AIMS: Coronary computed tomographic angiography (CCTA) is pivotal in diagnosing coronary artery disease (CAD). We explored the link between CAD severity and two biomarkers, Pan-Immune Inflammation Value (PIV) and Atherogenic Index of Plasma (AIP), in stable CAD patients. METHODS AND RESULTS: A retrospective observational study of 409 CCTA patients with stable angina pectoris. Logistic regression identified predictors of severe CAD, stratified by CAD-RADS score. Receiver Operating Characteristic (ROC) curves evaluated predictive performance. PIV and AIP were significant predictors of severe CAD (PIV: OR 1.002, 95% CI: 1.000-1.004, p < 0.021; AIP: OR 0.963, 95% CI: 0.934-0.993, p < 0.04). AUC values for predicting severe CAD were 0.563 (p < 0.001) for PIV and 0.625 (p < 0.05) for AIP. Combined with age, AUC improved to 0.662 (p < 0.02). CONCLUSIONS: PIV and AIP were associated with severe CAD, with AIP demonstrating superior predictive capability. Incorporating AIP into risk assessment could enhance CAD prediction, offering a cost-effective and accessible method for identifying individuals at high risk of coronary atherosclerosis.

Exploring the L-shaped relationship between Atherogenic Index of Plasma and depression: Results from NHANES 2005-2018.

BACKGROUND: The Atherogenic Index of Plasma (AIP) is a novel metric linked to several diseases. However, there is inadequate evidence to investigate the relationship between AIP and depression. Therefore, we aim to elucidate the non-linear association between AIP and depression. METHODS: 12,453 participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 were included. The AIP was calculated as log10 (triglycerides/high-density lipoprotein cholesterol). The Patient Health Questionnaire (PHQ-9) was used to identify depression (PHQ-9 ≥ 10). Weighted multivariate logistic regression, restricted cubic splines (RCS) models, subgroup analysis, and interaction tests were employed to reveal the relationship between AIP and depression. RESULTS: AIP was found to be significantly correlated with depression. In the fully adjusted model, elevated AIP levels were associated with higher odds of depression (odds ratio [OR] = 1.50; 95 % CI: 1.06-2.12). The RCS analysis indicated an L-shaped pattern in the relationship between depression and AIP, with inflection points at -0.289. Beyond this inflection point, individuals with elevated AIP levels were associated with higher odds of depression (OR = 2.25; 95 % CI: 1.49-3.39). Notably, the association was particularly pronounced among individuals with diabetes. LIMITATION: This cross-sectional study is unable to establish causal relationships. CONCLUSION: There was an L-shaped association between AIP and depression among US adults. AIP has the potential value as a biological marker for depression, and maintaining AIP values below a certain threshold may help in managing depression.

Gender differences in the association between changes in the atherogenic index of plasma and cardiometabolic diseases: a cohort study.

OBJECTIVE: The relationship between changes in Atherogenic Index of Plasma (AIP) and cardiometabolic diseases (CMD) in middle-aged and elderly individuals remains unclear. This study aims to explore the association between changes in AIP and CMD. METHODS: This study included 3,791 individuals aged over 45 years from CHARLS. Participants were divided into four groups using the K-Means clustering method. Cumulative AIP was used as a quantitative indicator reflecting changes in AIP. Differences in baseline data and CMD incidence rates among these four groups were compared. Multifactorial logistic regression models were used to assess the relationship between changes in AIP and CMD, and subgroup analysis and interaction tests were conducted to evaluate potential relationships between changes in AIP and CMD across different subgroups. Restricted cubic splines (RCS) were used to assess the dose-response relationship between cumulative AIP and CMD. RESULTS: Changes in AIP were independently and positively associated with CMD. In males, the risk significantly increased in class4 compared to class1 (OR 1.75, 95%CI 1.12-2.73). In females, changes in AIP were not significantly associated with CMD. Cumulative AIP was positively correlated with CMD (OR 1.15, 95%CI 1.01-1.30), with significant gender differences in males (OR 1.29, 95%CI 1.07-1.55) and females (OR 1.03, 95%CI 0.87-1.23) (p for interaction = 0.042). In addition, a linear relationship was observed between cumulative AIP and CMD in male. CONCLUSION: Substantial changes in AIP may increase the risk of CMD in middle-aged and elderly Chinese males. Dynamic monitoring of AIP is of significant importance for the prevention and treatment of CMD.

Atherogenic index of plasma as a predictor of coronary artery disease: a cohort study in south of Iran.

BACKGROUND: Coronary artery disease (CAD) is an atherosclerotic disease of an inflammatory nature. Previous studies examining the relationship between triglycerides and high-density lipoprotein cholesterol have highlighted the importance of plasma atherogenic index (AIP) as an important predictor of coronary heart disease. However, due to the lack of adequate information on this topic, this study aimed to investigate the relationship between AIP and coronary heart disease risk. RESULTS: This study included 2,226 women and 1,690 men aged 35-70 years who participated in the Bandar Kong Cohort study and met the eligibility criteria. The data was collected using a checklist and questionnaires, which were designed by experienced individuals. After participants completed a registration form and gave informed consent, face-to-face interviews were conducted by trained experts. The validity and reliability of the questionnaire had been verified by the national cohort team prior to its use. The Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.171) approved the study. Data from the initial cohort survey using SPSS software version 25, were analyzed to include several factors, including age, sex, smoking status, body mass index (BMI), physical activity level, socioeconomic status, AIP, systolic blood pressure, and diastolic blood pressure. The prevalence of coronary heart disease was found to be 7.5% higher in people with a BMI of 25 or higher. Also, Individuals with low physical activity had a higher prevalence. Individuals with CAD had significantly higher mean values for the AIP, age, systolic blood pressure, and diastolic blood pressure (0.46, 57.50, 128.43, and 81.10, respectively) compared to those without CAD. Furthermore, patients with CAD had lower years of education (2649.45 and 3.59) than individuals without CAD (P < 0.05). Importantly, our findings showed that AIP increased the odds ratio of coronary heart disease by 1.86 as an independent risk factor. CONCLUSIONS: Based on our investigation, the AIP is a valuable and independent predictive risk factor for coronary artery disease. This index can be utilized effectively due to its accessibility and affordability, making it a promising tool for risk assessment in clinical settings.

A Novel Score for an Old Enemy: Atherogenic Plasma Index Predicts In-Stent Restenosis among Stable Angina Pectoris Patients.

Objectives: Although the association of Atherogenic index of plasma (AIP) with coronary artery disease (CAD) and atherosclerosis is known, the relationship between AIP and in-stent restenosis (ISR) remains unclear. We aimed to investigate the relationship between AIP and ISR in patients with stable angina pectoris (SAP) treated with drug-eluting stent (DES). Methods: Patients with a history of DES implantation following stable angina were evaluated between January 2015 and November 2019 in this observational and retrospective study. 608 eligible patients were dichotomized into ISR+ (n=241) and ISR- (n=367). ISR was defined as the presence of 50% or greater stenosis. AIP was defined as log [TG/HDL-C]. Results: AIP levels were significantly higher in patients who developed ISR compared with those who did not (0.33 [0.15-0.52] vs 0.06 [-0.08-0.21] respectively, p<0.001). The AUC value of AIP levels for predicting ISR was 0.746 (p<0.001). Multivariate logistic regression analysis revealed that AIP, diabetes mellitus, higher LDL-C levels and lower LVEF values were independently associated with ISR. Conclusion: Multivariate analysis revealed that AIP was strongly independently associated with ISR. Using this novel inexpensive and easily calculable index may provide early recognition of ISR in patients with SAP who were treated with DES.

Elevated AIP is associated with the prevalence of MAFLD in the US adults: evidence from NHANES 2017-2018.

Background: Atherogenic Index of plasma (AIP) is closely related to metabolic abnormalities. But as of now, there is no definitive conclusion on the dose-response relationship pattern between AIP and metabolic associated fatty liver disease (MAFLD). Objective: The objective of this study was to provide a fresh insight for understanding the intrinsic link between AIP and the prevalence of MAFLD by exploring the dose-response pattern between AIP and MAFLD. Methods: A total of 9254 participants received the survey and 1090 participants were finally included according to the screening criteria. To evaluate the association between AIP and the prevalence of MAFLD based on weighted multivariate logistic regression. Sensitivity analysis of the association between AIP and MAFLD was performed using propensity score matching (PSM). Restrictive cubic splines (RCS) were used to identify patterns of dose-response relationships between AIP and MAFLD, and receiver operator characteristic (ROC) curves were used to evaluate the predictive ability of AIP and traditional lipid parameters for MAFLD. Results: In this study, a total of 563 participants were found to have MAFLD. The results of weighted multivariate logistic regression analysis demonstrated that, after adjusting for sex and age, participants in the highest quartile (Q4) of AIP had a significantly increased risk of developing MAFLD compared to those in the lowest quartile (Q1) (Model 2: OR = 9.03, 95% CI 4.75-17.17). A similar trend was observed in the fully adjusted model (Model 3: OR = 3.85, 95% CI 1.55-9.52). The RCS analysis revealed a linear dose-response association between AIP and MAFLD(P for crude non-linearity = 0.087). This association remained significant after accounting for potential confounding variables(P for adjusted non-linearity = 0.663). The ROC curve results suggest that AIP performs better than traditional lipid indicators in predicting MAFLD (AUC = 0.732, 95%CI 0.705-0.758). Conclusion: A linear dose-response relationship exists between AIP and MAFLD, suggesting that as AIP increases, so does the risk of developing MAFLD.

The clinical role of combined circulating complement C1q and AIP for CAD with LDL-C level below 1.8mmol/L.

BACKGROUND: In the past few years, circulating complement C1q involvement in atherosclerosis has garnered growing research interest in addition to the emerging recognition of the novel lipid marker named atherogenic index of plasma (AIP). Nevertheless, among patients experiencing low-density lipoprotein cholesterol (LDL-C) levels less than 1.8mmol/L, the interplay between C1q combined with the AIP for coronary artery disease (CAD) is ambiguous. METHODS: Patients were stratified into a non-CAD and CAD group according to their coronary angiography. The association between C1q in conjunction with the AIP and CAD was explored using restricted cubic spline analyses and logistic regression models. To assess how it predicted, a receiver operating characteristic analysis was undertaken. RESULTS: A total of 7270 patients comprised 1476 non-CAD patients and 5794 patients diagnosed with CAD were analyzed. A comparison of the two groups showed that the C1q levels were notably higher compared to the CAD group, while AIP exhibited an inverse trend. Across quartiles of C1q, the AIP demonstrated a decline with increasing C1q levels, and significant differences were observed between the groups. A correlation analysis underscored a notable negative correlation between the two variables. Univariate and multivariate logistic regression analyses revealed significant associations between CAD and the C1q quartile groups/AIP. Furthermore, compared with the Q4 group, a decrease in the C1q levels corresponded to an escalation in CAD risk, with the odds ratio rising from 1.661 to 2.314. CONCLUSIONS: In conclusion, there appears to be a notable positive correlation between the combination of C1q with the AIP and CAD.

The association between atherogenic index of plasma and metabolic dysfunction-associated fatty liver disease as detected by FibroScan / Asociación entre el índice aterogénico del plasma y la enfermedad hepática grasa asociadaa disfunción metabólica detectada por FibroScan

Objectives: this study aimed to explore the potential of the atherogenic index of plasma (AIP) as a predictor of metabolic dysfunction-associated fatty liver disease (MAFLD). Methods: a cross-sectional study, including data from 4473 participants in the National Health and Nutrition Examination Survey (NHANES) 2017-2018, was performed. A control attenuation parameter (CAP) ≥ 285 dB/m was used to confirm hepatic steatosis. Degrees of liver stiffness were confirmed according to liver stiffness measurement (LSM). Weighted multivariate logistic regression models were used to assess the association between AIP and the risk for MAFLD and liver fibrosis. Finally, receiver operating characteristic (ROC) curve analysis was used to test the accuracy of AIP in predicting MAFLD. Results: the association between AIP and the prevalence of MAFLD was positive in all three multivariate logistic regression models (model 1, odds ratio (OR), 18.2 (95 % confidence interval (CI), 14.4-23.1); model 2, OR, 17.0 (95 % CI, 13.3-21.8); model 3, OR, 5.2 (95 % CI, 3.9-7.0)). Moreover, this positive relationship was found to be significant in patients of different sexes and whether they had diabetes. However, no significant differences were observed between AIP and significant fibrosis or cirrhosis as assessed by different liver fibrosis indices. Finally, ROC curve analysis demonstrated that the AIP index also demonstrated positive diagnostic utility (area under the ROC curve, 0.733 (95 % CI, 0.718-0.747); p < 0.001). Conclusion: This study revealed a positive association between AIP and MAFLD among American adults. Furthermore, this association persisted in different sexes and whether they had diabetes.(AU)

Objetivos: este estudio tuvo como objetivo explorar el potencial del índice aterogénico del plasma (AIP) como predictor de enfermedad hepática grasa asociada a disfunción metabólica (MAFLD). Métodos: se realizó un estudio transversal que incluyó datos de 4473 participantes de la encuesta nacional de exémenes de salud y nutrición (NHANES) 2017-2018. Se utilizó un parámetro de atenuación de control (CAP) ≥ 285 dB/m para confirmar la esteatosis hepática. Los grados de rigidez hepática se confirmaron de acuerdo con la medición de rigidez hepática (LSM). Se utilizaron modelos de regresión logística multiva-riponderponderados para evaluar la asociación entre AIP y el riesgo de MAFLD y fibrosis hepática. Por último, se utilizó el análisis de la curva ROC para probar la precisión de la AIP en la predicción de la MAFLD.Resultados: la asociación entre AIP y prevalencia de MAFLD fue positiva en los tres modelos de regresión logística multivariable (modelo 1, odds ratio (OR): 18,2 (intervalo de confianza (IC) del 95 %: 14,4-23,1); Modelo 2, OR: 17,0 (IC del 95 %: 13,3-21,8); Modelo 3, OR: 5,2 (IC del 95 %: 3,9-7,0)). Además, esta relación positiva se encontró significativa en pacientes de diferentes sexos ya tuvieran o no diabetes. Sin embargo, no se observaron diferencias significativas entre la AIP y la fibrosis o cirrosis significativa evaluada por diferentes índices de fibrosis hepática. Finalmente, el análisis de la curva ROC demostró que el índice AIP también demostró utilidad diagnóstica positiva (área bajo la curva ROC = 0,733 (IC del 95 %: 0,718-0,747); p < 0,001). Conclusión: este estudio reveló una asociación positiva entre AIP y MAFLD en los adultos estadounidenses. Además, esta asociación persistióen los diferentes sexos ya tuvieran o no diabetes.(AU)

Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES.

BACKGROUND: Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or the atherogenic index of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is a lack of research on non-invasive and rapid prediction of cardiovascular risk. We aimed to develop and validate a machine-learning model for predicting cardiovascular risk based on variables encompassing clinical questionnaires and oculomics. METHODS: We collected data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training dataset (80% from the year 2008 to 2011 KNHANES) was used for machine learning model development, with internal validation using the remaining 20%. An external validation dataset from the year 2012 assessed the model's predictive capacity for TyG-index or AIP in new cases. We included 32122 participants in the final dataset. Machine learning models used 25 algorithms were trained on oculomics measurements and clinical questionnaires to predict the range of TyG-index and AIP. The area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score were used to evaluate the performance of our machine learning models. RESULTS: Based on large-scale cohort studies, we determined TyG-index cut-off points at 8.0, 8.75 (upper one-third values), 8.93 (upper one-fourth values), and AIP cut-offs at 0.318, 0.34. Values surpassing these thresholds indicated elevated cardiovascular risk. The best-performing algorithm revealed TyG-index cut-offs at 8.0, 8.75, and 8.93 with internal validation AUCs of 0.812, 0.873, and 0.911, respectively. External validation AUCs were 0.809, 0.863, and 0.901. For AIP at 0.34, internal and external validation achieved similar AUCs of 0.849 and 0.842. Slightly lower performance was seen for the 0.318 cut-off, with AUCs of 0.844 and 0.836. Significant gender-based variations were noted for TyG-index at 8 (male AUC=0.832, female AUC=0.790) and 8.75 (male AUC=0.874, female AUC=0.862) and AIP at 0.318 (male AUC=0.853, female AUC=0.825) and 0.34 (male AUC=0.858, female AUC=0.831). Gender similarity in AUC (male AUC=0.907 versus female AUC=0.906) was observed only when the TyG-index cut-off point equals 8.93. CONCLUSION: We have established a simple and effective non-invasive machine learning model that has good clinical value for predicting cardiovascular risk in the general population.