Development of a clinical prediction model using integrated epigenetics to predict COVID-19 severity
6 affiliations
- 1Department of Emergency Medicine, University of California Davis Medical Center, Sacramento, USA
- 2Department of Medical Microbiology and Immunology, University of California, Davis, USA
- 3Department of Pediatrics, University of California Davis Medical Center, Sacramento, USA
- 4Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, USA
- 5Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, USA
- 6University of Colorado Anschutz Medical Campus, Aurora, USA
Abstract
Background: Epigenetic markers, including DNA methylation (DNAm), can improve early disease diagnosis and risk stratification. The objective of this study was to evaluate whether the inclusion of a DNAm score would improve performance of a clinical prediction model for COVID-19 clinical severity.
Methods: We performed a retrospective cohort study of adults (>18 years) with COVID-19 who presented to the emergency department from March to June 2020, the initial peak of the COVID-19 epidemic. DNAm scores were performed on a subset of the cohort based upon availability of blood specimens, to characterize epigenetic signatures. Clinical data were obtained from an electronic data warehouse that includes clinical and biological variables. The primary outcome was a modified WHO (mWHO) ordinal score for COVID-19 clinical improvement, which stratifies severity based on respiratory support required. We performed univariate and bivariate analyses to further select variables for inclusion (p<0.05). We used ordinal logistic regression to develop a model predicting mWHO score. DNAm scores were measured at presentation using modified Infinium MethylationEPIC Arrays with 7,831 additional probes targeting 262 immune-enriched genes. We assessed the DNAm score’s incremental impact on predictive model performance, using the estimated logit-hat for the model derived for the entire cohort as an independent variable in a pair of models fit to the DNAm subsample; one with and the other without the score as a variable. The Wald test was used to quantify the incremental predictive value of the DNAm score. Discriminative capacity was quantified and compared using the c-statistic transformation of Somers D.
Results: A total of 1400 patients were included, 123 with DNAm scores. DNAm score had an adjusted odds ratio of 7.13 (95% CI 1.55-32.85; p=0.01). In comparing models with and without DNAm score, the c-statistic was 0.88 (95% CI 0.82-0.93; p<0.0001) without and 0.90 (95% CI 0.85-0.95; p<0.0001) with the DNAm score, an estimated difference in c-statistic of 0.02 (p=0.14).
Conclusion: The addition of DNAm scores at presentation improved prediction but not model discrimination in this small sample.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40246-026-01017-7.
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BibTeX
@article{arnold2026development,
title = {{Development of a clinical prediction model using integrated epigenetics to predict COVID-19 severity}},
author = {Cosby G. Arnold and James F. Holmes and Janine M. LaSalle and Daniel J. Tancredi and Iain R. Konigsberg and Ivana V. Yang and Michelle Edelmann and Monica Campbell and Kathleen C. Barnes and Andrew A. Monte},
journal = {Human Genomics},
year = {2026},
month = jul,
doi = {10.1186/s40246-026-01017-7},
url = {https://doi.org/10.1186/s40246-026-01017-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development of a clinical prediction model using integrated epigenetics to predict COVID-19 severity
AU - Cosby G. Arnold
AU - James F. Holmes
AU - Janine M. LaSalle
AU - Daniel J. Tancredi
AU - Iain R. Konigsberg
AU - Ivana V. Yang
AU - Michelle Edelmann
AU - Monica Campbell
AU - Kathleen C. Barnes
AU - Andrew A. Monte
PY - 2026
DA - 2026/07/28
JO - Human Genomics
DO - 10.1186/s40246-026-01017-7
UR - https://doi.org/10.1186/s40246-026-01017-7
AB - Background: Epigenetic markers, including DNA methylation (DNAm), can improve early disease diagnosis and risk stratification. The objective of this study was to evaluate whether the inclusion of a DNAm score would improve performance of a clinical prediction model for COVID-19 clinical severity. Methods: We performed a retrospective cohort study of adults (>18 years) with COVID-19 who presented to the emergency department from March to June 2020, the initial peak of the COVID-19 epidemic. DNAm scores were performed on a subset of the cohort based upon availability of blood specimens, to characterize epigenetic signatures. Clinical data were obtained from an electronic data warehouse that includes clinical and biological variables. The primary outcome was a modified WHO (mWHO) ordinal score for COVID-19 clinical improvement, which stratifies severity based on respiratory support required. We performed univariate and bivariate analyses to further select variables for inclusion (p<0.05). We used ordinal logistic regression to develop a model predicting mWHO score. DNAm scores were measured at presentation using modified Infinium MethylationEPIC Arrays with 7,831 additional probes targeting 262 immune-enriched genes. We assessed the DNAm score’s incremental impact on predictive model performance, using the estimated logit-hat for the model derived for the entire cohort as an independent variable in a pair of models fit to the DNAm subsample; one with and the other without the score as a variable. The Wald test was used to quantify the incremental predictive value of the DNAm score. Discriminative capacity was quantified and compared using the c-statistic transformation of Somers D. Results: A total of 1400 patients were included, 123 with DNAm scores. DNAm score had an adjusted odds ratio of 7.13 (95% CI 1.55-32.85; p=0.01). In comparing models with and without DNAm score, the c-statistic was 0.88 (95% CI 0.82-0.93; p<0.0001) without and 0.90 (95% CI 0.85-0.95; p<0.0001) with the DNAm score, an estimated difference in c-statistic of 0.02 (p=0.14). Conclusion: The addition of DNAm scores at presentation improved prediction but not model discrimination in this small sample. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40246-026-01017-7.
SN - 1473-9542
SN - 1479-7364
ER -