Preprint
Multidimensional Modeling to Maximize Adaptations to eXercise: The M 3 AX Trial Rationale and Study Design
05/28/2025
PMCID: PMC12148272
PMID: 40492097
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Abstract
Age-related functional declines are thought to be caused by hallmark biological processes that manifest in physical, mental, and metabolic impairments compromising intrinsic capacity, healthspan and quality-of-life. Exercise is a multipotent treatment with promise to mitigate most aging hallmarks, but there is substantial variability in individual exercise responsiveness. This inter-individual response heterogeneity (IRH) was first extensively interrogated by Bouchard and colleagues in the context of endurance training. Our group has interrogated IRH in response to resistance training and combined training, and we have conducted trials in older adults examining dose titration and adjuvant treatments in attempts to boost response rates. Despite the work of many groups, the mechanisms underpinning IRH and effective mitigation strategies largely remain elusive. The National Institute on Aging (NIA) hosted a focused workshop in 2022 titled “Understanding heterogeneity of responses to, and optimizing clinical efficacy of, exercise training in old adults”. This workshop spurred a dedicated NIA request for applications (RFA) with the major goal “to better understand factors underlying response variability to exercise training in older adults.” We developed a two-phase Sequential Multiple Assignment Randomized Trial (SMART) in response to the RFA that will allow us to classify individual responsiveness to combined endurance and resistance training and interrogate potential mechanistic underpinnings (Phase I), followed by an approach to boost responsiveness (Phase II). Using deep in vivo, ex vivo, and molecular phenotyping, we will establish multidimensional biocircuitry of responsiveness and build predictive models, providing a basis for personalized exercise prescriptions.
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Details
- Title
- Multidimensional Modeling to Maximize Adaptations to eXercise
- Resource Type
- Preprint
- Format
- link
- Number of pages
- 27
- Identifiers
- 99381380294906600
- Academic Unit
- Biology; Hal Marcus College of Science and Engineering
- Language
- English