Advances in exercise, fitness, and performance genomics in 2015

• Autores: Mark A. Sarzynski, Ruth J.F. Loos, Alejandro Lucía Mulas
• Localización: Medicine & Science in Sports & exercise: Official Journal of the American College of Sports Medicine, ISSN 0195-9131, Vol. 48, Nº. 10, 2016, págs. 1906-1916
• Idioma: inglés
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• Dialnet Métricas: 1 Cita
• Resumen

  • AB This review of the exercise genomics literature encompasses the highest-quality articles published in 2015 across seven broad topics: physical activity behavior, muscular strength and power, cardiorespiratory fitness and endurance performance, body weight and adiposity, insulin and glucose metabolism, lipid and lipoprotein metabolism, and hemodynamic traits. One study used a quantitative trait locus for wheel running in mice to identify single nucleotide polymorphisms (SNPs) in humans associated with physical activity levels. Two studies examined the association of candidate gene ACTN3 R577X genotype on muscular performance. Several studies examined gene-physical activity interactions on cardiometabolic traits. One study showed that physical inactivity exacerbated the body mass index (BMI)-increasing effect of an FTO SNP but only in individuals of European ancestry, whereas another showed that high-density lipoprotein cholesterol (HDL-C) SNPs from genome-wide association studies exerted a smaller effect in active individuals. Increased levels of moderate-to-vigorous-intensity physical activity were associated with higher Matsuda insulin sensitivity index in PPARG Ala12 carriers but not Pro12 homozygotes. One study combined genome-wide and transcriptome-wide profiling to identify genes and SNPs associated with the response of triglycerides (TG) to exercise training. The genome-wide association study results showed that four SNPs accounted for all of the heritability of [white up pointing triangle]TG, whereas the baseline expression of 11 genes predicted 27% of [white up pointing triangle]TG. A composite SNP score based on the top eight SNPs derived from the genomic and transcriptomic analyses was the strongest predictor of [DELTA]TG, explaining 14% of the variance. The review concludes with a discussion of a conceptual framework defining some of the critical conditions for exercise genomics studies and highlights the importance of the recently launched National Institutes of Health Common Fund program titled "Molecular Transducers of Physical Activity in Humans.