The risk and added values of the atherosclerotic cardiovascular risk enhancers on prediction of cardiovascular events: Tehran lipid and glucose study

Machine learning framework for atherosclerotic cardiovascular disease risk assessment

Introduction

Atherosclerotic cardiovascular disease (ASCVD) is the first leading cause of mortality globally. To identify the individual risk factors of ASCVD utilizing the machine learning (ML) approaches.

Materials & methods

This cohort-based cross-sectional study was conducted on data of 500 participants with ASCVD among Tabriz University Medical Sciences employees, during 2020. The data with ML methods were developed and validated to predict ASCVD risk with naive Bayes (NB), spurt vesture machines (SVM), regression tree (RT), k-nearest neighbors (KNN), artificial neural networks (ANN), generalized additive models (GAM), and logistic regression (LR).

Results

Accuracy of the models ranged from 95.7 to 98.1%, with a sensitivity of 50.0 to 97.3%, specificity of 74.3 to 99.1%, positive predictive value (PPV) of 0.0 to 98.0%, negative predictive value (NPV) of 68.4 to 100.0%, positive likelihood ratio (LR +) of 13.8 to 96.4%, negative likelihood ratio (LR-) of 3.6 to 51.9%, and area under ROC curve (AUC) of 62.5 to 99.4%. The ANN fit the data best with an accuracy of 98.1% (95% CI: 96.5-99.1), a specificity of 99.1% (95% CI: 97.7-99.9), a LR + of 96.4% (95% CI: 36.2-258.8), and AUC of 99.4% (95% CI: 85.2-97.0). Based on the optimal model, sex (females), age, smoking, and metabolic syndrome were shown to be the most important risk factors of ASCVD.

Conclusion

Sex (females), age, smoking, and metabolic syndrome were predictors obtained by ANN. Considering the ANN as the optimal model identified, more accurate prevention planning may be designed.

Does the addition of serum antimüllerian hormone concentrations to the Framingham Risk Score and Pooled Cohort Equations improve the prediction of cardiovascular disease?

The present study revealed that the addition of serum antimüllerian hormone concentrations to Framingham Risk Score and Pooled Cohort Equations could potentially improve the risk prediction of cardiovascular disease.

Objective

The current study aimed to examine the added value of serum antimüllerian hormone (AMH) concentration to the Framingham Risk Score (FRS) and Pooled Cohort Equations (PCE) in predicting the risk of cardiovascular disease (CVD) in women of reproductive age.

Methods

Women 30 years and older were considered eligible for this population-based prospective study. The univariate and multivariate Cox proportional hazard models were used to evaluate the association between the serum concentrations of AMH and the risk of CVD.

Results

In the enhanced model, which integrated AMH into FRS and PCE and was adjusted for family history of premature CVD, AMH showed a significant association with the risk of CVD during a 19-year follow-up of 800 women (hazard ratio, 0.77 [95% CI, 0.60-0.99] and hazard ratio, 0.64 [95% CI, 0.48-0.84], respectively). According to the likelihood-ratio test, the addition of AMH measurements to FRS and PCE could significantly improve the risk prediction of CVD (P = 0.02 and P < 0.001, respectively); however, the integration of this biomarker did not improve the classification of risk categories.

Conclusions

The present findings revealed that the addition of serum AMH concentrations to FRS and PCE could potentially improve the risk prediction of CVD.

Assessment of Cardiovascular Risk in Women: Progress so Far and Progress to Come

Cardiovascular disease is the leading cause of death in women worldwide. Nonetheless, there exist several uncertainties in the prediction, diagnosis, and treatment of cardiovascular disease in women. A cornerstone in the prediction of cardiovascular disease is the implementation of risk scores. A variety of pregnancy- and reproductive-factors have been associated with lower or higher risk of cardiovascular disease. Consequently, the question has been raised, whether these female-specific factors also provide added value to cardiovascular risk prediction. In this review, we provide an overview of the existing literature on sex differences in the association of established cardiovascular risk factors with cardiovascular disease and the relation between female-specific factors and cardiovascular risk. Furthermore, we systematically reviewed the literature for studies that assessed the added value of female-specific factors beyond already established cardiovascular risk factors. Adding female-specific factors to models containing established cardiovascular risk factors has led to little or no significant improvement in the prediction of cardiovascular events. However, analyses primarily relied on data from women aged ≥40 years. Future investigations are needed to quantify whether pregnancy-related factors improve cardiovascular risk prediction in young women in order to support adequate treatment of risk factors and enhance prevention of cardiovascular disease in women.

The association between low-density and non-high-density lipoprotein cholesterol with incident cardiovascular disease among low-risk Iranians during 2 decades follow-up

INTRODUCTION

To examine the associations between low-density and non-high-density lipoprotein cholesterol (LDL-C and non-HDL-C, respectively) with incident cardiovascular disease (CVD) in low-risk subjects.

MATERIALS AND METHODS

From a total of 2476 non-diabetic aged 40-70 years, free of CVD with LDL-C range 1.81≤LDL-C<4.91mmol/L with 10-year atherosclerotic cardiovascular disease (ASCVD) risk <7.5%, the associations of LDL-C and non-HDL-C with incident CVD were assessed using multivariable Cox proportional hazard regression analyses adjusted for age, sex, body mass index, waist circumference, HDL-C, triglycerides, chronic kidney disease, current smoking, hypertension, and family history of CVD.

RESULTS

During a median follow-up of 18 years, 559 CVD events occurred. Compared to the LDL-C <2.59 mmol/L as reference, the categories of 2.59≤LDL-C<3.36, 3.36≤LDL-C<4.14, and ≥4.14 mmol/L were associated with hazard ratios (95% confidence intervals) of 1.39(0.89-2.18), 1.72(1.11-2.68), and 2.19(1.36-3.51) for incident CVD (P for trend<0.0001), respectively. Compared to the non-HDL-C<3.36 as reference, the categories of 3.36≤non-HDL-C<4.14, 4.14≤non-HDL-C<4.91, and ≥4.91 mmol/L were associated with 1.48(0.96-2.30), 1.37(0.89-2.16), and 2.15(1.36-3.39) higher risk for incident CVD (P for trend <0.0001), respectively. Among those with ASCVD score <5% (n=2070), even the 2.59≤LDL-C<3.36 mmol/L increased the risk for CVD [1.73(1.01-2.97)]. Results for non-HDL-C categories remained unchanged except for the category of 4.14≤non-HDL-C<4.91 mmol/L that was not associated with CVD.

CONCLUSIONS

Among Iranian individuals with ASCVD risk as little as <5%, LDL-C≥2.59 mmol/L and non-HDL-C≥3.36 mmol/L, independent of traditional risk factors, were associated with a significantly higher risk of incident CVD, individuals that might potentially benefit from pharmacological therapy.

Prevalence and significance of risk enhancing biomarkers in the United States population at intermediate risk for atherosclerotic disease: Risk Enhancing Factors in Intermediate Risk for ASCVD

BACKGROUND

Pooled cohort equations (PCEs) estimate 10-year risk for atherosclerotic cardiovascular disease (ASCVD) in US adults. One use is to guide statin eligibility. However, PCEs risk estimate is inaccurate in some US subpopulations.

OBJECTIVE

Recent cholesterol guidelines proposed addition of risk enhancing factors to improve risk assessment for selection of statin therapy. This study examines frequencies of several risk enhancing biomarkers in NHANES subjects at intermediate risk (7.5 -<20% 10-year risk for ASCVD) and considers how they may be used to better assess risk for individuals.

METHODS

Prevalence of the following biomarkers were determined; elevations in apolipoprotein B-containing lipoproteins, i.e., LDL cholesterol (LDL-C) (160-189 mg/dL), non-HDL-cholesterol (non-HDL-C) (190-219 mg/dL), or total apolipoprotein B (apoB) (≥ 130 mg/dL), serum triglyceride (≥175 mg/dL), hemoglobin A1c (5.7-6.4%), high sensitivity C-reactive protein (2-10 mg/L), and waist circumference ≥ 102 cm, and abnormal estimated glomerular filtration rate (15 - ≤ 60 mg/min/1.73 m²).

RESULTS

25% of NHANES population had intermediate risk. In this subpopulation, 85% had ≥ 1 biomarkers-similarly in women and men-with a third having ≥3 abnormal markers. Frequencies were not age-related, except in those 40-49 years, in whom > 40% had ≥3 abnormal biomarkers. It made little difference whether LDL-C, non-HDL-C or apoB was used as the atherogenic lipoprotein.

CONCLUSION

Three or more enhancing risk factors in intermediate risk subjects can complement PCE-estimated 10-year risk and guide the patient-provider discussion toward use of lipid-lowering medication. Future research is needed to integrate risk estimates by PCE and multiple risk enhancers.