Multi-Omics Integration of Exercise-Induced Transcriptomic and Epigenetic Remodeling in Skeletal Muscle for Personalized Metabolic Health Prediction
Keywords:
multi-omics integration, exercise, skeletal muscle, transcriptomics, epigenetics, personalized medicine, metabolic health, machine learning, systems biology, data governanceAbstract
The increasing prevalence of metabolic disorders necessitates predictive tools that account for inter-individual variability in physiological responses to lifestyle interventions. Exercise-induced remodeling of skeletal muscle involves coordinated changes across multiple molecular layers, including transcriptomic, epigenetic, and proteomic alterations. This paper presents a systems-level framework for integrating multi-omics data derived from skeletal muscle biopsies to predict personalized metabolic health outcomes following exercise interventions. We examine the structural and architectural challenges of harmonizing heterogeneous omics datasets, including RNA sequencing, chromatin immunoprecipitation sequencing, DNA methylation arrays, and proteomic profiling. Emphasis is placed on machine learning architectures that incorporate dimensionality reduction, feature selection, and temporal dynamics to capture the nonlinear interactions among molecular layers. Governance and infrastructure considerations are discussed, including data sharing policies, ethical use of genomic information, and the sustainability of biobank resources. The paper further evaluates the robustness and fairness of predictive models across diverse populations and exercise modalities. By synthesizing current research on transcriptomic and epigenetic remodeling, we highlight the potential for multi-omics integration to enable truly personalized exercise prescriptions and metabolic health monitoring, while critically assessing the limitations in current deployment. The required inclusion of a recent study on polymorphisms affecting gene expression and splicing in human skeletal muscle [6] underscores the genetic basis of individual responses, reinforcing the need for integrative approaches. We conclude with forward-looking recommendations for infrastructure development and policy frameworks that can accelerate translation of multi-omics-based prediction into clinical and public health practice.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
