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The Role of Genetic and Metabolomic Profiling in Response to High-Intensity Interval Training (HIIT) Among Individuals with Moderate Fitness Levels | ||
| New Approaches in Exercise Physiology | ||
| دوره 6، شماره 11، شهریور 2024، صفحه 181-204 اصل مقاله (475.29 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22054/nass.2025.86341.1175 | ||
| نویسندگان | ||
| Mojtaba Ghorbani Asiabar* 1؛ Morteza Ghorbani Asiabar2؛ Alireza Ghorbani Asiabar2 | ||
| 1Post-doctorate in Physical Education, Farhangian University, Qazvin, Iran. | ||
| 2Payam Noor University, Qazvin, Iran | ||
| چکیده | ||
| Purpose: This study aimed to investigate the influence of genetic polymorphisms and metabolomic profiles on physiological adaptations to a 6-week High-Intensity Interval Training (HIIT) program in individuals with moderate fitness levels, addressing the variability in exercise response. Method: Thirty moderately fit adults participated in a supervised 6-week HIIT intervention. Pre- and post-training assessments included VO2max, lactate threshold, genetic profiling of key polymorphisms (e.g., PPARGC1A rs8192678) using PCR and next-generation sequencing, and untargeted metabolomic analysis via liquid chromatography-mass spectrometry (LC-MS). Statistical analyses involved paired t-tests, multivariate regression, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Results: Significant improvements were observed in VO2max (p < 0.001) and lactate threshold (p = 0.004). Carriers of the PPARGC1A G allele showed greater aerobic capacity gains, accompanied by upregulation of PGC-1α expression. Metabolomic profiling revealed significant shifts in glucose and lipid metabolism pathways post-HIIT. Multivariate models identified interactions between genetic variants and metabolomic changes that predicted individual training responsiveness. Conclusion: Integrating genetic and metabolomic data enhances understanding of individual variability in HIIT adaptations and supports the development of personalized exercise prescriptions to optimize health and performance outcomes. | ||
| کلیدواژهها | ||
| Mitochondrial Biogenesis؛ Personalized Exercise؛ Aerobic Capacity؛ Genetic Polymorphism؛ Metabolic Adaptation | ||
| مراجع | ||
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Alvarez-Romero, J., Voisin, S., Eynon, N., & Hiam, D. (2021). Mapping robust genetic variants associated with exercise responses. International journal of sports medicine, 42(01), 3–18.
Alvarez Romero, J. (2023). The molecular architecture of trainability explained by genetics Victoria University].
Bottura, R. M., & Dentillo, D. B. (2025). Genomics May Be the Key to Understanding Endurance Training Pillars. Genes, 16(3), 338. Bouchard, C., Antunes-Correa, L. M., Ashley, E. A., Franklin, N., Hwang, P. M., Mattsson, C. M., Negrao, C. E., Phillips, S. A., Sarzynski, M. A., & Wang, P.-y. (2015). Personalized preventive medicine: genetics and the response to regular exercise in preventive interventions. Progress in cardiovascular diseases, 57(4), 337–346.
Czajkowski, S. M., Arteaga, S. S., & Burg, M. M. (2022). Social support and cardiovascular disease. In Handbook of Cardiovascular Behavioral Medicine (pp. 605–630). Springer.
Egan, B., & Zierath, J. R. (2013). Exercise metabolism and the molecular regulation of skeletal muscle adaptation. Cell Metabolism, 17(2), 162–184.
Friedrich, V. K., Rubel, M. A., & Schurr, T. G. (2022). Mitochondrial genetic variation in human bioenergetics, adaptation, and adult disease. American Journal of Human Biology, 34(2), e23629.
Ghorbani Asiabar, M., Ghorbani Asiabar, M., & Ghorbani Asiabar, A. (2023). The Impact of Gut Microbiome Modulation on Athletic Performance and Post-Exercise Recovery in Endurance Runners. New Approaches in Exercise Physiology, 5(10), 114–132. https://doi.org/10.22054/nass.2024.66557.1161
Ghorbani Asiabar, M., Ghorbani Asiabar, M., & Ghorbani Asiabar, A. (2024). Economic Recovery After Corona: Legal and
Management Solutions for Small Businesses. In Preprints: Preprints.
Hernández-Lepe, M. A., Hernández-Ontiveros, D. A., ChávezGuevara, I. A., Ramos-Jiménez, A., Hernández-Torres, R. P., López-Fregoso, R. J., Ramos-Lopez, O., Amaro-Gahete, F. J., Muñiz-Salazar, R., & Olivas-Aguirre, F. J. (2024). Impact of Exercise Training at Maximal Fat Oxidation Intensity on Metabolic and Epigenetic Parameters in Patients with Overweight and Obesity: Study Protocol of a Randomized Controlled Trial. Journal of Functional Morphology and Kinesiology, 9(4), 214.
Noone, J., Mucinski, J. M., DeLany, J. P., Sparks, L. M., & Goodpaster, B. H. (2024). Understanding the variation in exercise responses to guide personalized physical activity prescriptions. Cell Metabolism, 36(4), 702–724.
Petr, M., Stastny, P., Zajac, A., Tufano, J. J., & Maciejewska-Skrendo, A. (2018). The role of peroxisome proliferator-activated receptors and their transcriptional coactivators gene variations in human trainability: a systematic review. International journal of molecular sciences, 19(5), 1472.
Ramos Jimenez, A. (2024). Impact of Exercise Training at Maximal Fat Oxidation Intensity on Metabolic and Epigenetic Parameters in Patients with Overweight and Obesity: Study Protocol of a Randomized Controlled Trial. Instituto de Ciencias Biomédicas.
Timmons, J. A., Knudsen, S., Rankinen, T., Koch, L. G., Sarzynski,
M., Jensen, T., Keller, P., Scheele, C., Vollaard, N. B., & Nielsen, S. (2010). Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. Journal of applied physiology, 108(6), 1487–1496.
Varillas-Delgado, D. (2024). Role of the PPARGC1A Gene and Its rs8192678 Polymorphism on Sport Performance, Aerobic Capacity, Muscle Adaptation and Metabolic Diseases: A Narrative Review. Genes, 15(12), 1631.
Williams, C. J., Williams, M. G., Eynon, N., Ashton, K. J., Little, J. P., Wisloff, U., & Coombes, J. S. (2017). Genes to predict VO 2max trainability: a systematic review. BMC genomics, 18, 81– 110. | ||
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