Does exercise adaptation vary with genetics? Original paper

We all adapt to exercise differently. This meta-analysis attributes a large part of this variance to genetics: 44% for endurance, 72% for strength, and 10% for power. Genetics research is still in its infancy, however, so those numbers are to be taken with a grain of salt, and we’re still far from being able to predict an individual’s response to exercise based on his or her genotype.

This Study Summary was published on January 4, 2022.

Background

Endurance, strength, and power are the three main aspects of exercise fitness. While everyone can improve them through exercise, some people have an easier time of it. Genetics may explain up to 80% of the interindividual variance in exercise adaptation. Various genes and polymorphisms (gene variations) are involved, and identifying them all will be the work of many, many studies.

The study

This meta-analysis of 24 studies included 3,012 healthy, untrained people aged 18–55. The interventions lasted ≥2 weeks, did not involve dietary manipulations, and included one of the following forms of exercise:

  • Endurance (75% of VO2max).
  • Strength (75% of lower-body 1RM).
  • Power (90–110% of PPO).

The meta-analysis compared the preintervention outcomes with the postintervention outcomes for each study. To assess the influence of genetics on the changes observed, the meta-analysis divided the participants into genetic subgroups.

The results

Endurance, strength, and power increased by 11%, 22%, and 12% over the course of the interventions. The change in endurance was not statistically significant.

Thirteen candidate genes and their associated alleles were identified and associated with endurance (9 genes), strength (6), and power (4):

Potential effects of genes on exercise adaptation

image
L = large; M = medium; S = small

Note

There are plenty of reasons why we should consider these results preliminary.

  • The number of study groups for each gene was rather small (2–9; median 4), as was the number of participants per study (17–743; median 52).

  • People with greater expression of certain genes adapted better to exercise (by gaining more muscle from the same workout, for instance), which suggests that these genes improved exercise adaptation. Most studies didn’t evaluate gene variants, however, and different variants of the same gene might have different effects.

  • A given phenotype (or “genetic effect”) is often the result of, not a single gene, but the interactions of several genes,[1] and we probably don’t yet know all the genes involved.

  • We’re not even sure that genes with similar expression profiles have related functions, or conversely, that genes with dissimilar expression profiles don’t share a function (meaning that they could achieve the same effect, just via different pathways).

  • Differences in analysis methods (differences in dataset choice, quality control, or clustering approach, for instance) can make it more difficult to identify functional associations.[2]

  • Most genetic research is based on gene expression, which is measured by evaluating mRNA expression; yet mRNA expression doesn’t always translate to an actual effect on the body.

The big picture

A 2016 meta-analysis of data from 479 twins and siblings attributed more than half of interindividual differences in VO2max to genetics.[6]

A 2017 systematic review of 32 studies identified 97 genes that might predict VO2max trainability. It suggests that phenotypes depend on combinations of genes (and gene variants), rather than on individual genes (or gene variants).[1]

A 2015 controlled trial (an 18-week endurance-training program for 206 adults) demonstrated that different genetic variants of a specific ACTN3 gene (i.e., XX, RR, RX) could affect oxygen uptake differently.[7] This trial was included in the meta-analysis under review, which found that, although the effect size was small for all the variants, it was nonsignificant only for the XX variant.[8] The XX gene variant has also been associated with poorer strength and power.[9]

Several studies have demonstrated that training adaptation depends on both gene expression (RNA levels) and the specific fitness outcomes measured. Which individual genes (or gene variants) or combinations of genes (or gene variants) are involved, however, isn’t always clear.[10][11] Moreover, the genome-wide association studies that generally drive these gene-outcome associations do not always account for nongenetic factors that can influence the outcomes (such as baseline training status[12] and proper rest[13]). And even when they do try to account for various such confounders, they cannot possibly set perfect case-control comparisons.

Still, despite their many limitations, studies looking for gene-outcome associations increase our understanding of how genes, gene variants, and their combinations affect how we each respond to various external factors,[14] such as exercise, as we saw here, or caffeine, as we saw in a previous Study Summary.[15]

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This Study Summary was published on January 4, 2022.

References

  1. ^Camilla J Williams, Mark G Williams, Nir Eynon, Kevin J Ashton, Jonathan P Little, Ulrik Wisloff, Jeff S CoombesGenes to predict VO 2max trainability: a systematic reviewBMC Genomics.(2017 Nov 14)
  2. ^Sahra Uygun, Cheng Peng, Melissa D Lehti-Shiu, Robert L Last, Shin-Han ShiuUtility and Limitations of Using Gene Expression Data to Identify Functional AssociationsPLoS Comput Biol.(2016 Dec 9)
  3. ^Ann-Bin Shyu, Miles F Wilkinson, Ambro van HoofMessenger RNA regulation: to translate or to degradeEMBO J.(2008 Feb 6)
  4. ^Jean Hausser, Avi Mayo, Leeat Keren, Uri AlonCentral dogma rates and the trade-off between precision and economy in gene expressionNat Commun.(2019 Jan 8)
  5. ^Ronald C Taylor, Bobbie-Jo M Webb Robertson, Lye Meng Markillie, Margrethe H Serres, Bryan E Linggi, Joshua T Aldrich, Eric A Hill, Margaret F Romine, Mary S Lipton, H Steven WileyChanges in translational efficiency is a dominant regulatory mechanism in the environmental response of bacteriaIntegr Biol (Camb).(2013 Nov)
  6. ^Nienke M Schutte, Ineke Nederend, James J Hudziak, Meike Bartels, Eco J C de GeusTwin-sibling study and meta-analysis on the heritability of maximal oxygen consumptionPhysiol Genomics.(2016 Mar)
  7. ^Michelle S M Silva, Wladimir Bolani, Cleber R Alves, Diogo G Biagi, José R Lemos Jr, Jeferson L da Silva, Patrícia A de Oliveira, Guilherme B Alves, Edilamar M de Oliveira, Carlos E Negrão, José E Krieger, Rodrigo G Dias, Alexandre C PereiraElimination of influences of the ACTN3 R577X variant on oxygen uptake by endurance training in healthy individualsInt J Sports Physiol Perform.(2015 Jul)
  8. ^Henry C Chung, Don R Keiller, Justin D Roberts, Dan A GordonDo exercise-associated genes explain phenotypic variance in the three components of fitness? a systematic review & meta-analysisPLoS One.(2021 Oct 14)
  9. ^Juan Del Coso, Danielle Hiam, Peter Houweling, Laura M Pérez, Nir Eynon, Alejandro LucíaMore than a 'speed gene': ACTN3 R577X genotype, trainability, muscle damage, and the risk for injuriesEur J Appl Physiol.(2019 Jan)
  10. ^Sigal Ben-Zaken, Alon Eliakim, Dan Nemet, Yoav MeckelGenetic Variability Among Power Athletes: The Stronger vs. the FasterJ Strength Cond Res.(2019 Jun)
  11. ^Peter K Davidsen, Iain J Gallagher, Joseph W Hartman, Mark A Tarnopolsky, Flemming Dela, Jørn W Helge, James A Timmons, Stuart M PhillipsHigh responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expressionJ Appl Physiol (1985).(2011 Feb)
  12. ^Mark D Peterson, Matthew R Rhea, Brent A AlvarApplications of the dose-response for muscular strength development: a review of meta-analytic efficacy and reliability for designing training prescriptionJ Strength Cond Res.(2005 Nov)
  13. ^J Parra, J A Cadefau, G Rodas, N Amigó, R CussóThe distribution of rest periods affects performance and adaptations of energy metabolism induced by high-intensity training in human muscleActa Physiol Scand.(2000 Jun)
  14. ^Vivian Tam, Nikunj Patel, Michelle Turcotte, Yohan Bossé, Guillaume Paré, David MeyreBenefits and limitations of genome-wide association studiesNat Rev Genet.(2019 Aug)
  15. ^Kalliopi G Gkouskou, Georgios Georgiopoulos, Ioannis Vlastos, Evgenia Lazou, Dimitrios Chaniotis, Theodore G Papaioannou, Christos S Mantzoros, Despina Sanoudou, Aristides G EliopoulosCYP1A2 polymorphisms modify the association of habitual coffee consumption with appetite, macronutrient intake, and body mass index: results from an observational cohort and a cross-over randomized studyInt J Obes (Lond).(2021 Sep 25)