Date of Award
Open Access Dissertation
Mark A. Sarzynski
Background: To investigate the molecular mechanisms, determinants, and predictors of cardiometabolic responses to regular exercise, this dissertation had two primary aims: 1) Examine the molecular architecture of cardiometabolic responses to exercise training (unsupervised approach) and 2) Identify molecular signatures of responsiveness to regular exercise within clinically relevant lipoprotein traits (supervised approach).
Methodology: Multi-omics data (17,945 muscle transcripts, 4,979 proteins, 411 metabolites) from participants (n=647) who completed 20 weeks of endurance training as part of the HERITAGE Family Study was collected before and after training. We employed an unsupervised machine learning approach (Aim 1) to identify patterns of molecular responses to training and then examined the association between molecular responses and concomitant cardiometabolic adaptations (e.g., VO2max, visceral fat, HDL-C, insulin). In Aim 2 we utilized supervised machine learning approaches to identify molecular determinants exercise-induced changes in plasma lipid traits and examine the utility of circulating molecules for predicting lipid responses to an exercise intervention.
Results: In Aim 1, we identified 13 distinct modules of skeletal muscle transcripts, 6 of circulating proteins, and 5 of circulating metabolites that change in similar manners with training with large inter-individual differences in molecular responses. Identified modules were associated with concomitant adaptations to VO2max, %Fat, and inflammation, providing insight into the pathways associated with training-induced changes in these traits. In Aim 2 we found hundreds of circulating molecules associated with lipid-trait responses to training. Baseline abundance of 5 proteins (PTPRS, IL1-R1, SLITRK3, MAdCAM-1, and CSH) were associated with exercise-induced changes in more than one lipid trait. Molecules associated with lipid responses to exercise were overrepresented in pathways associated with immune function. We also demonstrate that circulating molecules may improve the prediction of individual responses of lipid traits to regular exercise.
Conclusions: Complex molecular profiles and molecular changes with exercise likely underlie the many benefits of exercise training. The increasing availability of multi-omic data represents a promising resource for understanding why exercise is beneficial for health. Here we provide an in-depth examination of the cardiometabolic benefits of exercise training in the context of large-scale multi-omics data and identify molecules and pathways that warrant further investigation toward improving cardiometabolic health.
Barber, J. L.(2022). Molecular Architecture of Cardiometabolic Responses to Regular Exercise. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6564
Available for download on Friday, May 31, 2024