A simple, rapid, interpretable, actionable and implementable digital PCR based mortality index. Issue 10 (3rd October 2021)

• Record Type: Journal Article
• Authors: Philibert, Robert; Long, Jeffrey D.; Mills, James A.; Beach, S. R. H.; Gibbons, Frederick X.; Gerrard, Meg; Simons, Ron; Pinho, Paulo B.; Ingle, Douglas; Dawes, Kelsey; Dogan, Timur; Dogan, Meeshanthini
• Is Part Of: Epigenetics. Volume 16:Issue 10(2021)
• Page Start: 1135
• Page End: 1149
• Publication Date: 2021-10-03
• Subjects: DNA methylation -- mortality -- smoking -- alcohol -- diabetes -- coronary artery disease; Epigenesis -- Periodicals; Epigenetica; 572.86505
• DOI: 10.1080/15592294.2020.1841874
• Languages: English
• ISSNs: 1559-2294

Abstract

ABSTRACT: 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.