Dyslipidemia and coronary artery calcium: From association to development of a risk-prediction nomogram

Predicting the risk of coronary artery calcium progression in the general population: insights from the MESA and CARDIA studies

AIM

Coronary artery calcium (CAC) progression is a strong predictor of cardiovascular disease. This study aims to develop and validate a practical tool for predicting individual CAC progression in the general population.

MATERIALS AND METHODS

Data were utilized from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort, comprising 5486 participants (47.3% male, mean ± SD age: 61.9 ± 10.2 years), who were randomly assigned to either a training set or an internal validation set at a 7:3 ratio. Additionally, a separate cohort of 2447 participants (44.6% male, mean ± SD age: 40.4 ± 3.5 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) study served as the external validation set. A nomogram was developed based on a Cox regression model incorporating 10 variables selected by the least absolute shrinkage and selection operator (LASSO) method to predict CAC progression.

RESULTS

From the 61 features considered, 10 key variables were identified: age, male sex, smoking status, waist circumference, systolic blood pressure, fasting glucose, lipid abnormalities, and the use of antihypertensive, glucose-lowering, and lipid-lowering medications. The nomogram demonstrated good discrimination with a C-statistic of 0.682 (95% confidence interval [CI], 0.665-0.699) in the training set and 0.750 (95% CI, 0.729-0.771) in the external validation set. Decision curve analysis further confirmed the nomogram's clinical utility in predicting the risk of CAC progression.

CONCLUSION

Our nomogram offers a practical tool for individualized prediction of CAC progression potentially aiding in the primary prevention of cardiovascular disease in clinical practice. REGISTRATION URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT00005130 (CARDIA), NCT00005487 (MESA).

An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data

Background

Dyslipidemia has emerged as a significant clinical risk, with its associated complications, including atherosclerosis and ischemic cerebrovascular disease, presenting a grave threat to human well-being. Hence, it holds paramount importance to precisely predict the onset of dyslipidemia. This study aims to use ensemble technology to establish a machine learning model for the prediction of dyslipidemia.

Methods

This study included three consecutive years of physical examination data of 2,479 participants, and used the physical examination data of the first two years to predict whether the participants would develop dyslipidemia in the third year. Feature selection was conducted through statistical methods and the analysis of mutual information between features. Five machine learning models, including support vector machine (SVM), logistic regression (LR), random forest (RF), K nearest neighbor (KNN) and extreme gradient boosting (XGBoost), were utilized as base learners to construct the ensemble model. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the model.

Results

Experimental results show that the ensemble model achieves superior performance across several metrics, achieving an AUC of 0.88 ± 0.01 (P < 0.001), surpassing the base learners by margins of 0.04 to 0.20. Calibration curves and DCA exhibited good predictive performance as well. Furthermore, this study explores the minimal necessary feature set for accurate prediction, finding that just the top 12 features were required for dependable outcomes. Among them, HbA1c and CEA are key indicators for model construction.

Conclusions

Our results suggest that the proposed ensemble model has good predictive performance and has the potential to become an effective tool for personal health management.

From Cells to Plaques: The Molecular Pathways of Coronary Artery Calcification and Disease

Coronary artery calcification (CAC) is a hallmark of atherosclerosis and a critical factor in the development and progression of coronary artery disease (CAD). This review aims to address the complex pathophysiological mechanisms underlying CAC and its relationship with CAD. We examine the cellular and molecular processes that drive the formation of calcified plaques, highlighting the roles of inflammation, lipid accumulation, and smooth muscle cell proliferation. Additionally, we explore the genetic and environmental factors that contribute to the heterogeneity in CAC and CAD presentation among individuals. Understanding these intricate mechanisms is essential for developing targeted therapeutic strategies and improving diagnostic accuracy. By integrating current research findings, this review provides a comprehensive overview of the pathways linking CAC to CAD, offering insights into potential interventions to mitigate the burden of these interrelated conditions.

Coronary artery calcification burden, atherogenic index of plasma, and risk of adverse cardiovascular events in the general population: evidence from a mediation analysis

BACKGROUND

Dyslipidemia and abnormal cholesterol metabolism are closely related to coronary artery calcification (CAC) and are also critical factors in cardiovascular disease death. In recent years, the atherogenic index of plasma (AIP) has been widely used to evaluate vascular sclerosis. This study aimed to investigate the potential association of AIP between CAC and major adverse cardiovascular events (MACEs).

METHODS

This study included 1,121 participants whose CACs were measured by multislice spiral CT. Participants' CAC Agatston score, CAC mass, CAC volume, and number of vessels with CACs were assessed. AIP is defined as the base 10 logarithm of the ratio of triglyceride (TG) concentration to high-density lipoprotein-cholesterol (HDL-C) concentration. We investigated the multivariate-adjusted associations between AIP, CAC, and MACEs. The mediating role of the AIP in CAC and MACEs was subsequently discussed.

RESULTS

During a median follow-up of 31 months, 74 MACEs were identified. For each additional unit of log-converted CAC, the MACE risk increased by 48% (HR 1.48 [95% CI 1.32-1.65]). For each additional unit of the AIP (multiplied by 10), the MACEs risk increased by 19%. Causal mediation analysis revealed that the AIP played a partial mediating role between CAC (CAC Agatston score, CAC mass) and MACEs, and the mediating proportions were 8.16% and 16.5%, respectively. However, the mediating effect of CAC volume tended to be nonsignificant (P = 0.137).

CONCLUSIONS

An increased AIP can be a risk factor for CAC and MACEs. Biomarkers based on lipid ratios are a readily available and low-cost strategy for identifying MACEs and mediating the association between CAC and MACEs. These findings provide a new perspective on CAC treatment, early diagnosis, and prevention of MACEs.

Associations between insulin resistance indices and subclinical atherosclerosis: A contemporary review

Even in the absence of hyperglycemia or hyperlipidemia, it has been demonstrated that insulin resistance is an independent risk factor for atherosclerosis. Finding markers of insulin resistance that are associated with markers of atherosclerosis could help identify patients early in their disease course and allow for earlier initiation of preventative treatments. We reviewed available evidence regarding associations between known markers of insulin resistance and known markers of atherosclerosis. Serum triglycerides (TG), triglyceride-glucose index (TyG), and homeostasis model assessment (HOMA) were the insulin resistance markers reviewed. The coronary artery calcium score (CAC), carotid intimal medium thickness (cIMT), and pulse wave velocity (PWV) were reviewed as markers of atherosclerosis. TyG showed the most consistent association with CAC across broad demographic groups, though HOMA showed potential in obese individuals and those without diabetes. The data regarding cIMT and the reviewed insulin resistance markers did not yield any consistent associations, though very elevated TyG did appear to be associated with cIMT among normal weight individuals. Serum triglycerides showed a strong and consistent association with PWV across numerous studies and populations, though TyG index also demonstrated a strong association with PWV in a large systematic review. Of the insulin resistance markers reviewed, the TyG index appears to be most consistently associated with markers of atherosclerosis. TyG can be easily calculated with routine labwork and has the potential to inform decisions regarding early initiation of therapies in patients who would otherwise not be treated. Targeting insulin sensitivity prior to the development of T2DM has the potential to reduce development and progression of atherosclerosis, and patients without T2DM but who have elevated TyG index should be the topic of further research.

HDL-cholesterol concentration and its association with coronary artery calcification: a systematic review and meta-analysis

BACKGROUND

Coronary artery calcification (CAC) is a potential risk marker of coronary atherosclerosis that has high specificity and sensitivity. However, the association between high-density lipoprotein cholesterol (HDL-C) concentration and CAC incidence and progression is controversial.

METHODS

PubMed, Embase, Web of Science, and Scopus were systematically searched to identify relevant observational studies up to March 2023 and assessed the methodological quality using Newcastle-Ottawa Scale (NOS) scale. Random-effects meta-analysis was used to estimate pooled odds ratios (OR) and 95% confidence interval considering heterogeneity across studies.

RESULTS

Of the 2,411 records, 25 cross-sectional (n = 71,190) and 13 cohort (n = 25,442) studies were included in the systematic review. Ten cross-sectional and eight cohort studies were not eligible and were omitted from the meta-analysis. A total of 15 eligible cross-sectional studies (n = 33,913) were included in the meta-analysis and pooled results revealed no significant association between HDL-C and CAC > 0, CAC > 10, or CAC > 100 [pooled OR: 0.99 (0.97, 1.01)]. Meta-analysis of the 5 eligible prospective cohort studies (n = 10,721) revealed no significant protective effect of high HDL-C against CAC > 0 [pooled OR: 1.02 (0.93, 1.13)].

CONCLUSIONS

According to this analysis of observational studies, high HDL-C levels were not found to predict protection against CAC. These results suggest HDL quality rather than HDL quantity is important for certain aspects of atherogenesis and CAC.

REGISTRATION NUMBER

CRD42021292077.

Wake-up ischemic stroke associated with short sleep duration and sleep behavior: A stratified analysis according to risk of obstructive sleep apnea

OBJECTIVE

Wake-up stroke (WUS) is an ischemic stroke occurring during nocturnal sleep with neurological deficits observed upon awakening. Our study aimed to investigate the association between WUS, sleep curtailment, and sleep behavior according to the obstructive sleep apnea (OSA) risk in patients with acute ischemic stroke.

METHODS

This single-centered, retrospective study included hospitalized subjects with acute ischemic stroke occurring within 30 days. A total of 250 participants were classified as WUS or not and enquired about their sleep habits concerning sleep time on weekdays and weekends, demographic factors, and assessed comorbid medical conditions. Weekend catch-up sleep (CUS) was defined as the extension of sleep duration during weekends. The average weekly sleep duration and chronotype were assessed. The association between WUS and sleep factors was analyzed.

RESULTS

WUS was observed in 70 patients (28.0%) with acute ischemic stroke. There were no significant differences in the demographic and stroke-related variables between the WUS and non-WUS (NWUS) groups. Upon stratified analysis based on risk of OSA, average weekly sleep duration (odds ratio, [OR] = 0.60, 95% confidence interval, [CI] = 0.41-0.89; p = 0.011), the presence of weekend CUS (OR = 0.07, 95% CI = 0.01-0.97; p = 0.047), and chronotype (OR = 0.62, 95% CI = 0.39-0.98; p = 0.041) were independently associated with WUS in low-risk group with OSA, but not in the high-risk group.

CONCLUSIONS

Short sleep duration and lack of compensation are significantly associated with WUS in low-risk OSA group. Insufficient sleep and sleep behaviors could play a different role in causing ischemic stroke during sleep when patients are stratified by their risk of sleep apnea.

Imaging subclinical coronary atherosclerosis to guide lipid management, are we there yet?

Atherosclerotic cardiovascular disease risk (ASCVD) is an ongoing epidemic, and lipid abnormalities are its primordial cause. Most individuals suffering a first ASCVD event are previously asymptomatic and often do not receive preventative therapies. The cornerstone of primary prevention has been the identification of individuals at risk through risk calculators based on clinical and laboratory traditional risk factors plus risk enhancers. However, it is well accepted that a clinical risk calculator misclassifies a significant proportion of individuals leading to the prescription of a lipid-lowering medication with very little yield or a missed opportunity for lipid-lowering agents with a potentially preventable event. The development of coronary artery calcium scoring (CAC) and CT coronary angiography (CCTA) provide complementary tools to directly visualize coronary plaque and other risk-modifying imaging components that can potentially provide individualized lipid management. Understanding patient selection for CAC or potentially CCTA and the risk implications of the different parameters provided, such as CAC score, coronary stenosis, plaque characteristics and burden, epicardial adipose tissue, and pericoronary adipose tissue, have grown more complex as technologies evolve. These parameters directly affect the shared decision with patients to start or withhold lipid-lowering therapies, to adjust statin intensity or LDL cholesterol goals. Emerging lipid lowering studies with non-invasive imaging as a guide to patient selection and treatment efficacy, plus the evolution of lipid lowering therapies from statins to a diverse armament of newer high-cost agents have pushed these two fields forward with a complex interaction. This review will discuss existing risk estimators, and non-invasive imaging techniques for subclinical coronary atherosclerosis, traditionally studied using CAC and more recently CCTA with qualitative and quantitative measurements. We will also explore the current data, gaps of knowledge and future directions on the use of these techniques in the risk-stratification and guidance of lipid management.

Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment

OBJECTIVES

To calculate the coronary artery calcification score (CACS) obtained from coronary artery computed tomography angiography (CCTA) examination and combine it with the influencing factors of coronary artery calcification (CAC), which is then analyzed by machine learning (ML) to predict the probability of coronary heart disease(CHD).

METHODS

All patients who were admitted to the Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University from January 2019 to March 2022, suspected of CHD, and underwent CCTA inspection were retrospectively selected. The degree of CAC was quantified based on the Agatston score. To compare the correlation between the CACS and clinical-related factors, we collected 31 variables, including hypertension, diabetes, smoking, hyperlipidemia, among others. ML models containing the random forest (RF), radial basis function neural network (RBFNN),support vector machine (SVM),K-Nearest Neighbor algorithm (KNN) and kernel ridge regression (KRR) were used to assess the risk of CHD based on CACS and clinical-related factors.

RESULTS

Among the five ML models, RF achieves the best performance about accuracy (ACC) (78.96%), sensitivity (SN) (93.86%), specificity(Spe) (51.13%), and Matthew's correlation coefficient (MCC) (0.5192).It also has the best area under the receiver operator characteristic curve (ROC) (0.8375), which is far superior to the other four ML models.

CONCLUSION

Computer ML model analysis confirmed the importance of CACS in predicting the occurrence of CHD, especially the outstanding RF model, making it another advancement of the ML model in the field of medical analysis.