The Association between the Atherogenic Index of Plasma and Cardiometabolic Risk Factors: A Review

The positive association between the atherogenic index of plasma and the risk of new-onset hypertension: a nationwide cohort study in China

BACKGROUND

The atherogenic index of plasma (AIP) is a novel metabolic biomarker of atherosclerosis. Nevertheless, the association between the AIP and new-onset hypertension has not been elucidated in the Chinese population.

METHODS

Prospective data were obtained from 3150 participants aged ≥ 18 years in the China Health and Nutrition Survey from 2009 to 2015. The AIP is a logarithmically transformed ratio of triglycerides to high-density lipoprotein cholesterol in molar concentration. Cox regression analysis was used to determine the association of AIP index with new-onset hypertension.

RESULTS

After the six-year follow-up, 1054 (33.4%) participants developed new-onset hypertension. The participants were divided into AIP quartile groups (Q1-Q4). Compared with those in Q1, subjects in Q3-4 had nearly 1.35 times the risk of new-onset hypertension after full adjustment [Q3: hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.13-1.62; Q4: HR: 1.35, 95% CI: 1.13-1.64]. The risks of new-onset hypertension were nearly 1.30 times higher in subjects in Q2-4 than in subjects in Q1 (p < .01) after the full adjustment when we excluded subjects with diabetes and/or chronic kidney diseases. There was a significant difference [HR (CI): 1.27 (1.04-1.54) vs. 0.90 (0.69-1.18)] when subjects were divided into two groups according to body mass index (BMI) level (<24 vs. ≥24 kg/m2).

CONCLUSIONS

The present study suggested that individuals with a higher AIP index are associated with new-onset hypertension, independent of kidney function and glucose levels. The association was stronger in subjects with normal BMI, which may provide early screening of metabolomics in hypertension prevention.

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.

Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population

Background

Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients.

Methods

This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model.

Results

The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model.

Conclusion

The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke: a retrospective cohort study

BACKGROUND

Atherogenic index of plasma (AIP) has been demonstrated as a surrogate marker for ischemic stroke, but there is limited evidence for the effect of long-term elevation of AIP on ischemic stroke. Therefore, we aimed to characterize the relationship between cumulative exposure to AIP and the risk of ischemic stroke.

METHODS

A total of 54,123 participants in the Kailuan Study who attended consecutive health examinations in 2006, 2008, and 2010 and had no history of ischemic stroke or cancer were included. The time-weighted cumulative AIP (cumAIP) was calculated as a weighted sum of the mean AIP values for each time interval and then normalized to the total duration of exposure (2006-2010). Participants were divided into four groups according to quartile of cumAIP: the Q1 group, ≤-0.50; Q2 group, - 0.50 to - 0.12; Q3 group, - 0.12 to 0.28; and Q4 group, ≥ 0.28. Cox proportional hazard models were used to evaluate the relationship between cumAIP and ischemic stroke by calculating hazard ratios (HRs) and 95% confidence intervals (95% CIs).

RESULTS

After a median follow-up of 11.03 years, a total of 2,742 new ischemic stroke events occurred. The risk of ischemic stroke increased with increasing quartile of cumAIP. After adjustment for potential confounders, Cox regression models showed that participants in the Q2, Q3, and Q4 groups had significantly higher risks of ischemic stroke than those in the Q1 group. The HRs (95% CIs) for ischemic stroke in the Q2, Q3, and Q4 groups were 1.17 (1.03, 1.32), 1.33 (1.18, 1.50), and 1.45 (1.28, 1.64), respectively. The longer duration of high AIP exposure was significantly associated with increased ischemic stroke risk.

CONCLUSIONS

High cumulative AIP is associated with a higher risk of ischemic stroke, which implies that the long-term monitoring and maintenance of an appropriate AIP may help prevent such events.

Atherogenic Index of Plasma Predicts Obstructive Coronary Artery Disease in Patients with Stable Angina Pectoris

AIMS

Chronic coronary syndrome is associated with several risk factors, such as dyslipidemia and hypertension. The atherogenic index of plasma (AIP) has been demonstrated to be a biochemical risk factor for coronary artery disease (CAD). This study aimed to determine whether the AIP is an effective parameter for estimating obstructive CAD.

METHODS AND RESULTS

A total of 345 patients (with a mean age of 62.2 ± 10.3; 63% male) who underwent coronary angiography were included in this study. Obstructive CAD is defined as having one or more vessels with a stenosis level of ≥50%. Depending on the presence of obstructive CAD, all patients were divided into two groups. The mean AIP value was found to be 0.538 ± 0.26 in the study group. The AIP values were significantly higher in the obstructive coronary artery group (AIP; 0.49 ± 0.26 vs. 0.58 ± 0.27, p = 0.002). According to a univariable analysis, AIP values were significantly associated with obstructive coronary artery disease [OR: 3.74 (CI 95% 1.62-8.64), p = 0.020]. The AIP was further adjusted for confounding risk factors in three multivariable analysis models and, all three models showed a significant association. According to an ROC analysis, 0.49 is the cut-off value for AIP, and a value above 0.49 indicates 50% coronary artpery stenosis.

CONCLUSIONS

The AIP may be used in the assessment of cardiovascular risk for patients with stable angina pectoris, and it may also be used to estimate obstructive CAD.

Association of Atherogenic Index of Plasma with Cardiometabolic Risk Factors and Markers in Lean 14-to-20-Year-Old Individuals: A Cross-Sectional Study

Cardiometabolic risk factors at a young age pose a significant risk for developing atherosclerotic cardiovascular disease in adulthood. Atherogenic dyslipidemia is highly associated with obesity and metabolic syndrome already in young age. It remains unclear whether cardiometabolic risk factors associate with the atherogenic index of plasma (AIP = log (TAG/HDL-C) in lean subjects with low atherogenic risk. As both the AIP and markers of cardiometabolic risk are continuous variables, we expected their association to be linear before the manifestation of obesity and atherogenic dyslipidemia. We analyzed the prevalence of increased atherogenic risk (AIP ≥ 0.11) in 2012 lean 14-to-20-year-old subjects (55% females) and the trends of cardiometabolic risk factors across the quartiles (Q) of AIP in a subgroup of 1947 (56% females) subjects with low atherogenic risk (AIP < 0.11). The prevalence of AIP ≥ 0.11 reached 3.6% in females and 8.5% in males. HDL-C, non-HDL-C, triglycerides, and the continuous metabolic syndrome score showed a stepwise worsening across the AIP quartiles in both sexes. Measures of obesity and insulin resistance were worse in Q4 vs. Q1 groups, and leukocyte counts were higher in Q4 and Q3 vs. Q1. Females in Q4 presented with a higher C-reactive protein and lower adiponectin, estradiol, and testosterone levels. The multivariate regression model selected non-HDL-C, QUICKI, and erythrocyte counts as significant predictors of AIP in males; and non-HDL-C and C-reactive protein in females. A question arises whether the lean individuals on the upper edge of low atherogenic risk are prone to earlier manifestation of metabolic syndrome and shift to the higher AIP risk group.

Special Issue: New Insight into the Molecular Role of Lipids and Lipoproteins in Vascular Diseases

Lipids and lipoproteins play a key role in cardiovascular diseases (CVD), mainly in the development of atherosclerosis [...].

The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China

BACKGROUND

The atherogenic index of plasma (AIP) is an important alternative metabolic biomarker of atherosclerosis and cardiovascular diseases. Nevertheless, the correlation between the AIP and carotid atherosclerosis is unknown among the general population.

METHODS

A total of 52,380 community residents, aged ≥ 40 years who underwentcervical vascular ultrasound from December 2017 to December 2020 in Hunan China, were selected for retrospective analysis. The AIP was calculated as a logarithmically converted ratio of triglycerides (TG) to high-density lipoprotein-cholesterol (HDL-C). The participants were divided into AIP quartile groups (Q1-Q4). Logistic regression models and restricted cubic spline analyses were used to examine the association of the AIP with carotid atherosclerosis. Stratified analyses were applied to control for confounding factors. The incremental predictive value of the AIP was further assessed.

RESULTS

After adjusting for traditional risk factors, an increased AIP was associated with a higher rate of carotid atherosclerosis (CA), increased carotid intima-media thickness (CIMT), and plaques [odds ratio, OR (95% confidence interval, CI): 1.06 (1.04, 1.08), 1.07 (1.05, 1.09), and 1.04 (1.02, 1.06) per 1-SD increase in the AIP, respectively]. Compared with those participants in the quartile 1 group, those in the quartile 4 group had a greater risk of CA [OR 1.18, 95% CI (1.12, 1.25)], increased CIMT [OR 1.20, 95% CI (1.13, 1.26)], and plaques [OR 1.13, 95% CI (1.06, 1.19)]. However, we did not observe an association between the AIP and stenosis [0.97 (0.77, 1.23), p for trend = 0.758]. Restricted cubic spline analyses also showed a cumulative increase in the risk of CA, increased CIMT, and plaques but not stenosis severity (> 50%) with an increase of the AIP. Subgroup analyses showed that a more significant association between the AIP and the prevalence of increased CA was detected in younger subjects (aged < 60 years) with a body mass index (BMI) of ≥ 24 and fewer comorbidities. Additionally, the AIP provided incremental predictive capacity over established risk factors for CA, as shown by an improvement in the net reclassification index (NRI) and integrated discrimination index (IDI) (all P < 0.05).

CONCLUSIONS

An elevated AIP in a community-based population is associated with a higher rate of CA. the AIP could serve as a potential biomarker for CA risk assessment.