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

Lioy, Beatrice; Webb, R.P.; Amirabdollahian, Farzad

doi:10.3390/healthcare11070966

Cited by 18 publications

(9 citation statements)

References 94 publications

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“…An analysis of 32 articles revealed that a large waist circumference (WC), high triglyceride (TG) levels, high levels of insulin resistance (IR), and low high-density lipoprotein cholesterol (HDLC) concentrations were strongly correlated with increased AIP values. There were a few studies that examined blood pressure (BP), and the results were inconsistent [30]. Based on these studies, it can be concluded that the AIP is associated with WC, TG, IR, and HDL-C.…”

Section: Discussionmentioning

confidence: 99%

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

Kurklu,

Tan,

Ozyuncu

et al. 2023

Diagnostics

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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.

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Section: Discussionmentioning

confidence: 99%

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

Kurklu,

Tan,

Ozyuncu

et al. 2023

Diagnostics

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“…These properties are the initial stages of atherogenesis. Second, the AIP plays an important role in regulating the reverse cholesterol transport process, which associated with the recycling or disposal of excess cholesterol [ 54 ]. Increased AIP may mean adipocytes to store excess TG as fat, increase the accumulation of the cholesterol crystals in the inner layers of the atherosclerotic arteries, cause the narrowing and blockage of the lumen, and eventually lead to the formation of atherosclerosis.…”

Section: Discussionmentioning

confidence: 99%

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

Huang

Liu

Wei

et al. 2023

Cardiovasc Diabetol

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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.

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“…The AIP, CRI, METS-IR, and TyG index are metabolism-related parameters commonly used in the diagnosis and risk assessment of metabolism-related diseases ( 15 – 18 ). The current study showed that three metabolism-related parameters, including AIP, CRI, and TyG, were significantly higher in the SCAS group than in the control group ( P < 0.05) ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

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

Tusongtuoheti,

Shu,

Huang

et al. 2024

Front. Endocrinol.

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BackgroundCardiovascular 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.MethodsThis 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.ResultsThe 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.ConclusionThe 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.

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

Cited by 18 publications

References 94 publications

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

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

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

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

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