Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance
Abstract Background Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA),...
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BMC
2024-12-01
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| Series: | Clinical Epigenetics |
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| Online Access: | https://doi.org/10.1186/s13148-024-01784-x |
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| author | Anna Großbach Matthew J. Suderman Anke Hüls Alexandre A. Lussier Andrew D. A. C. Smith Esther Walton Erin C. Dunn Andrew J. Simpkin |
| author_facet | Anna Großbach Matthew J. Suderman Anke Hüls Alexandre A. Lussier Andrew D. A. C. Smith Esther Walton Erin C. Dunn Andrew J. Simpkin |
| author_sort | Anna Großbach |
| collection | DOAJ |
| description | Abstract Background Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA. Results Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered advanced GrimAge in individuals assigned male at birth that decelerates over time. Conclusion Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked. |
| format | Article |
| id | doaj-art-d4846f4ff3084a0b8d2d4e495bc5cd01 |
| institution | Kabale University |
| issn | 1868-7083 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Clinical Epigenetics |
| spelling | doaj-art-d4846f4ff3084a0b8d2d4e495bc5cd012024-12-22T12:33:35ZengBMCClinical Epigenetics1868-70832024-12-0116111210.1186/s13148-024-01784-xMaximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidanceAnna Großbach0Matthew J. Suderman1Anke Hüls2Alexandre A. Lussier3Andrew D. A. C. Smith4Esther Walton5Erin C. Dunn6Andrew J. Simpkin7School of Mathematical and Statistical Sciences, University of GalwayMRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolDepartment of Epidemiology, Rollins School of Public Health, Emory UniversityPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalMathematics and Statistics Research Group, University of the West of EnglandDepartment of Psychology, University of BathPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalSchool of Mathematical and Statistical Sciences, University of GalwayAbstract Background Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA. Results Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered advanced GrimAge in individuals assigned male at birth that decelerates over time. Conclusion Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.https://doi.org/10.1186/s13148-024-01784-xEpigenetic ageLongitudinal studiesALSPACAccelerated agingDNA methylation |
| spellingShingle | Anna Großbach Matthew J. Suderman Anke Hüls Alexandre A. Lussier Andrew D. A. C. Smith Esther Walton Erin C. Dunn Andrew J. Simpkin Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance Clinical Epigenetics Epigenetic age Longitudinal studies ALSPAC Accelerated aging DNA methylation |
| title | Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance |
| title_full | Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance |
| title_fullStr | Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance |
| title_full_unstemmed | Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance |
| title_short | Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance |
| title_sort | maximizing insights from longitudinal epigenetic age data simulations applications and practical guidance |
| topic | Epigenetic age Longitudinal studies ALSPAC Accelerated aging DNA methylation |
| url | https://doi.org/10.1186/s13148-024-01784-x |
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