Research Paper|Volume 17, Issue 5|pp 1105—1138

APOE genotype and biological age impact inter-omic associations related to bioenergetics

Dylan Ellis¹, Kengo Watanabe^1,¹⁵, Tomasz Wilmanski¹, Michael S. Lustgarten², Andres V. Ardisson Korat³, Gwênlyn Glusman¹, Jennifer Hadlock^1,⁴, Oliver Fiehn⁵, Paola Sebastiani⁶, Nathan D. Price^9,¹², Leroy Hood^1,^7,^8,^10,^11,¹², Andrew T. Magis¹, Simon J. Evans^8,¹², Lance Pflieger^8,¹², Jennifer C. Lovejoy^1,^8,¹², Sean M. Gibbons^1,^7,^13,¹⁴, Cory C. Funk¹, Priyanka Baloni^1,¹⁶, Noa Rappaport^1,^8,¹²

¹Institute for Systems Biology, Seattle, WA 98109, USA
²Metabolism and Basic Biology of Aging, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111, USA
³Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111, USA
⁴Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
⁵West Coast Metabolomics Center, University of California, Davis, CA 95616, USA
⁶Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA
⁷Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
⁸Phenome Health, Seattle, WA 98109, USA
⁹Thorne HealthTech, New York, NY 10019, USA
¹⁰Department of Immunology, University of Washington, Seattle, WA 98195, USA
¹¹Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
¹²Buck Institute for Research on Aging, Novato, CA 94945, USA
¹³eScience Institute, University of Washington, Seattle, WA 98195, USA
¹⁴Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
¹⁵Present address: Department of Medical Artificial Intelligence and Data Science, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
¹⁶Present address: School of Health Sciences, Purdue University, West Lafayette, IN 47907, USA

Received: December 16, 2024Accepted: April 22, 2025Published: May 3, 2025

https://doi.org/10.18632/aging.206243

Copyright: © 2025 Ellis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Apolipoprotein E (APOE) modifies human aging; specifically, the ε2 and ε4 alleles are among the strongest genetic predictors of longevity and Alzheimer’s disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. In the present study, we analyzed multi-omic association patterns across APOE genotypes, sex, and biological age (BA) axes in 2,229 community dwelling individuals. Our analysis, supported by validation in an independent cohort, identified diacylglycerols as the top APOE-associated plasma metabolites. However, despite the known opposing aging effects of the allele variants, both ε2- and ε4-carriers showed higher diacylglycerols compared to ε3-homozygotes. ‘Omics association patterns of ε2-carriers and increased biological age were also counter-intuitively similar, displaying significantly increased associations between insulin resistance markers and energy-generating pathway metabolites. These results demonstrate the context-dependence of the influence of APOE, with ε2 potentially strengthening insulin resistance-like pathways in the decades prior to imparting its longevity benefits. Additionally, they provide an atlas of APOE-related ‘omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.