External validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease

Dogan, Meeshanthini V.; Knight, Stacey; Dogan, Timur; Knowlton, Kirk U.; Philibert, Robert A.

doi:10.2217/epi-2021-0123

Cited by 12 publications

(8 citation statements)

References 46 publications

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“…The accuracy, sensitivity and specificity obtained using the random forest classifier were 0.78, 0.75 and 0.80, respectively. In a 2021 study by Meeshanthini V Dogan et al [45], an ensemble genetic performance genetic model for predicting 3-year coronary events was developed. This model showed a sensitivity of 0.79, a specificity of 0.75, a sensitivity of 0.15 and a specificity of 0.93 on the test set.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

Yang

Zhongju

Yang³

et al. 2023

IEEE Access

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Coronary heart disease (CHD) is a dangerous condition that cannot be completely cured. Accurate detection of early coronary artery disease can assist physicians in treating patients. In this study, a prediction model called HY_OptGBM was proposed for predicting CHD by using the optimized LightGBM classifier. To optimize the LightGBM classifier, the hyperparameters of the LightGBM model were adjusted. In addition, its loss function was improved, and the model was trained using adjusted hyperparameters. In this study, the hyperparameters of the prediction model were optimized by applying the most advanced hyperparameter optimization framework (OPTUNA). The improved loss function is referred to as the focal loss (FL). In this study, a prediction model was evaluated by using CHD data from the Framingham Heart Institute. To evaluate the performance of the prediction model, various metrics, including precision, recall, F score, accuracy, MCC, sensitivity, specificity, and AUC, were used. The AUC value of the proposed model was 97.9%, which was better than that of other comparative models. The results demonstrate that the rate of early identification of CHD among the general population can be improved by utilizing the proposed method. This, in turn, could serve to mitigate the costs associated with the medical treatment of patients suffering from CHD.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

Yang

Zhongju

Yang³

et al. 2023

IEEE Access

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“…These data were then subjected to the same quality control procedure as the FHS cohort. 3 In total, 862 593 methylation and 818 046 SNP loci survived quality control measures. For the data mining analyses, we retained loci common to both the Illumina Infinium HumanMethylation450 BeadChip array and Illumina Infinium MethylationEpic Beadchip array arrays, resulting in 437 242 loci being available for further analysis.…”

Section: Methodsmentioning

confidence: 99%

“…In previous work, we have shown that artificial intelligence (AI)‐aided diagnostic approaches incorporating epigenetic (DNA methylation) effects and gene‐methylation interactions (GxMeth) can be translated into clinically implementable methods that can outperform standard lipid‐based approaches in predicting 3‐year risk for the onset of symptomatic CHD. 3 However, whether clinical translation could be successfully achieved for similar in silico findings 4 for the detection of stable CHD has remained uncertain. Instead, current guidelines for assessing the presence of stable CHD stipulate the use of established measures such as the use of nonemergent exercise stress tests with ECGs or coronary computed tomographic angiography, depending on factors such as the degree of clinical suspicion, the ability to exercise, and the likelihood of high‐risk events.…”

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confidence: 99%

Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease

Philibert,

Dogan,

Knight

et al. 2023

JAHA

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Background Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic–epigenetic tools may provide a new avenue for assessing CHD. Methods and Results In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence–guided, clinically implementable method that uses input from 6 methylation‐sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine‐phospho‐guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches. Conclusions We conclude that this scalable integrated genetic–epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation‐sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.

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“…Genetic risk factors have become increasingly well-established over the past decade with the increased use of genome-wide association studies (GWAS) to identify disease-susceptibility loci [ 132 , 133 , 134 ]. However, epigenetic diagnostic markers and therapeutic targets, independent of genetic inheritance, have also been identified [ 135 , 136 , 137 ]. While associations between the risk factors and the clinical outcomes are commonly acknowledged, the cascade of events proceeding via cell aging, presenting as overt changes in tissue and organ function, and ultimately as clinical disease, are less well appreciated.…”

Section: Future Approaches To Cardiovascular Disease Interventionmentioning

confidence: 99%

A Unified Model of Age-Related Cardiovascular Disease

Fossel

Bean

Khera

et al. 2022

Biology

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Despite progress in biomedical technologies, cardiovascular disease remains the main cause of mortality. This is at least in part because current clinical interventions do not adequately take into account aging as a driver and are hence aimed at suboptimal targets. To achieve progress, consideration needs to be given to the role of cell aging in disease pathogenesis. We propose a model unifying the fundamental processes underlying most age-associated cardiovascular pathologies. According to this model, cell aging, leading to cell senescence, is responsible for tissue changes leading to age-related cardiovascular disease. This process, occurring due to telomerase inactivation and telomere attrition, affects all components of the cardiovascular system, including cardiomyocytes, vascular endothelial cells, smooth muscle cells, cardiac fibroblasts, and immune cells. The unified model offers insights into the relationship between upstream risk factors and downstream clinical outcomes and explains why interventions aimed at either of these components have limited success. Potential therapeutic approaches are considered based on this model. Because telomerase activity can prevent and reverse cell senescence, telomerase gene therapy is discussed as a promising intervention. Telomerase gene therapy and similar systems interventions based on the unified model are expected to be transformational in cardiovascular medicine.

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External validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease

Cited by 12 publications

References 46 publications

Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease

A Unified Model of Age-Related Cardiovascular Disease

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