The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs.

INTRODUCTION: The cost of secondary prevention of coronary heart disease (CHD) is continuing to increase, with a substantial portion of this acceleration being driven by the expense of confirmatory diagnostic testing. Conceivably, newly developed precision epigenetic technologies could drive down these costs. However, at the current time, their impact on overall expense for CHD care is poorly understood. We hypothesized that the use of a newly developed, highly sensitive, and specific epigenetic test, PrecisionCHD, could decrease the costs of secondary prevention. METHODS: To test this hypothesis, we constructed a budget impact analysis using a cost calculation model that examined the effects of substituting PrecisionCHD for conventional CHD diagnostic tests on the expenses of the initial evaluation and first year of care of stable CHD using a 1-year time horizon with no discounting. RESULTS: The model projected that for a commercial insurer with one million members, full adoption of PrecisionCHD as the primary method of initial CHD assessment would save approximately $113.6 million dollars in the initial year. CONCLUSION: These analyses support the use of precision epigenetic methods as part of the initial diagnosis and care of stable CHD and can meaningfully reduce cost. Real-world pilots to test the reliability of these analyses are indicated.

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

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.

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

Aim: The Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equation (PCE) for predicting risk for incident coronary heart disease (CHD) work poorly. To improve risk stratification for CHD, we developed a novel integrated genetic-epigenetic tool. Materials & methods: Using machine learning techniques and datasets from the Framingham Heart Study (FHS) and Intermountain Healthcare (IM), we developed and validated an integrated genetic-epigenetic model for predicting 3-year incident CHD. Results: Our approach was more sensitive than FRS and PCE and had high generalizability across cohorts. It performed with sensitivity/specificity of 79/75% in the FHS test set and 75/72% in the IM set. The sensitivity/specificity was 15/93% in FHS and 31/89% in IM for FRS, and sensitivity/specificity was 41/74% in FHS and 69/55% in IM for PCE. Conclusion: The use of our tool in a clinical setting could better identify patients at high risk for a heart attack.

Lay abstract Current lipid-based methods for assessing risk for coronary heart disease (CHD) have limitations. Conceivably, incorporating epigenetic information into risk prediction algorithms may be beneficial, but underlying genetic variation obscures its effects on risk. In order to develop a better CHD risk assessment method, we used artificial intelligence to identify genome-wide genetic and epigenetic biomarkers from two independent datasets of subjects characterized for incident CHD. The resulting algorithm significantly outperformed the current assessment methods in independent test sets. We conclude that artificial intelligence-moderated genetic-epigenetic algorithms have considerable potential as clinical tools for assessing risk for CHD.

Cost-utility analysis of an integrated genetic/epigenetic test for assessing risk for coronary heart disease.

Aim: New epigenetically based methods for assessing risk for coronary heart disease may be more sensitive but are generally more costly than current methods. To understand their potential impact on healthcare spending, we conducted a cost-utility analysis. Methods: We compared costs using the new Epi + Gen CHD™ test with those of existing tests using a cohort Markov simulation model. Results: We found that use of the new test was associated with both better survival and highly competitive negative incremental cost-effectiveness ratios ranging from -$42,000 to -$8000 per quality-adjusted life year for models with and without a secondary test. Conclusion: The new integrated genetic/epigenetic test will save money and lives under most real-world scenarios. Similar advantages may be seen for other epigenetic tests.

A simple, rapid, interpretable, actionable and implementable digital PCR based mortality index.

Mortality assessments are conducted for both civil and commercial purposes. Recent advances in epigenetics have resulted in DNA methylation tools to assess risk and aid in this task. However, widely available array-based algorithms are not readily translatable into clinical tools and do not provide a good foundation for clinical recommendations. Further, recent work shows evidence of heritability and possible racial bias in these indices. Using a publicly available array data set, the Framingham Heart Study (FHS), we develop and test a five-locus mortality-risk algorithm using only previously validated methylation biomarkers that have been shown to be free of racial bias, and that provide specific assessments of smoking, alcohol consumption, diabetes and heart disease. We show that a model using age, sex and methylation measurements at these five loci outperforms the 513 probe Levine index and approximates the predictive power of the 1030 probe GrimAge index. We then show each of the five loci in our algorithm can be assessed using a more powerful, reference-free digital PCR approach, further demonstrating that it is readily clinically translatable. Finally, we show the loci do not reflect ethnically specific variation. We conclude that this algorithm is a simple, yet powerful tool for assessing mortality risk. We further suggest that the output from this or similarly derived algorithms using either array or digital PCR can be used to provide powerful feedback to patients, guide recommendations for additional medical assessments, and help monitor the effect of public health prevention interventions.

Rooting carbon dioxide removal research in the social sciences.

Reports from a variety of bodies have highlighted the role that carbon dioxide removal (CDR) technologies and practices must play in order to try to avoid the worst effects of anthropogenic climate change. Research into the feasibility of these technologies is primarily undertaken by scholars in the natural sciences, yet, as we argue in this commentary, there is great value in collaboration between these scholars and their colleagues in the social sciences. Spurred by this belief, in 2019, a university and a non-profit organization organized and hosted a workshop in Washington, DC, intended to bring natural and physical scientists, technology developers, policy professionals and social scientists together to explore how to better integrate social science knowledge into the field of CDR research. The workshop sought to build interdisciplinary collaborations across CDR topics, draft new social science research questions and integrate and exchange disciplinary-specific terminology. But a snowstorm kept many social scientists who had organized the conference from making the trip in person. The workshop went on without them and organizers did the best they could to include the team remotely, but in the age before daily video calls, remote participation was not as successful as organizers had hoped. And thus, a workshop that was supposed to focus on social science integration moved on, without many of the social scientists who organized the event. The social scientists in the room were supposed to form the dominant voice but with so many stuck in a snow storm, the balance of expertise shifted, as it often does when social scientists collaborate with natural and physical scientists. The outcomes of that workshop, lessons learned and opportunities missed, form the basis of this commentary, and they collectively indicate the barriers to integrating the natural, physical and social sciences on CDR. As the need for rapid, effective and successful CDR has only increased since that time, we argue that CDR researchers from across the spectrum must come together in ways that simultaneously address the technical, social, political, economic and cultural elements of CDR development, commercialization, adoption and diffusion if the academy is to have a material impact on climate change in the increasingly limited window we have to address it.