Research Paper Volume 17, Issue 5 pp 1221—1260
Methods for joint modeling of longitudinal omics data and time-to-event outcomes: applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study
- 1 Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27708, USA
- 2 Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
- 3 Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA
- 4 Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, MO 63130, USA
- 5 Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- 6 G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- 7 Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital, New York, NY 10032, USA
- 8 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
- 9 Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
Received: August 2, 2024 Accepted: May 16, 2025 Published: May 27, 2025
https://doi.org/10.18632/aging.206259How to Cite
Copyright: © 2025 Arbeev 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
Studying the relationships between longitudinal changes in omics variables and event risks requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study, focusing on the understudied associations of longitudinal changes in lysophosphatidylcholines (LPCs) with mortality and aging-related outcomes. We analyzed 23 LPC species, with 5,066 measurements of each in 3,462 participants, 1,245 of whom died during follow-up. JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest magnitude observed for LPC 15:0/0:0 (hazard ratio: 0.71, 95% CI (0.64, 0.79)). SPM applications to LPC 15:0/0:0 revealed that the JM association reflects underlying aging-related processes: a decline in robustness to deviations from optimal LPC levels, higher equilibrium LPC levels in females, and the opposite age-related changes in the equilibrium and optimal LPC levels (declining and increasing, respectively), which lead to increased mortality risks with age. Our results support LPCs as biomarkers of aging and related decline in biological robustness, and call for further exploration of factors underlying age-related changes in LPC in relation to mortality and diseases.
Abbreviations
AL: allostatic load; APOE: apolipoprotein E; BMI: body mass index; CI: confidence interval(s); H0s: null hypotheses; HR: hazard ratio(s); JM: joint model(s); JM-SRE: joint model(s) with shared random effects; LLFS: Long Life Family Study; LPC: lysophosphatidylcholine(s); PCs: principal components; SPM: stochastic process model(s); SPPB: Short Physical Performance Battery.