Conventional exercise prescription is comprised of blanket advice. For example, the American College of Sports Medicine (ACSM) recommend >150 minutes of moderate-intensity and >75 minutes of vigorous-intensity cardiovascular exercise per week, along with resistance training twice per week with repetition ranges of 8-12 for novices and 1-12 for intermediates [1, 2]. Within this advice is the implicit assumption that humans respond in a predictable nature to known exercise inputs. Given these recommendations, you might think that an individual’s adaptive response to an exercise intervention is a predictable, standardised phenomenon tightly distributed around an averaged group mean. Yet, in recent decades, studies designed to examine the individual adaptive response to exercise have illustrated large inter-individual variations, in both the magnitude and direction of the resulting response, exceeding far beyond both the expected day-to-day biological perturbations [3. 4], and our conventional perspectives [5, 6].

Historically, training theory has been founded on the implicit, and previously unexamined, assumption that adaptation to training is both standardised and predictable across individuals. Such assumptions form the conceptual bedrock of periodisation theory and exercise prescription literature within sports coaching, public health and medical domains [1, 2, 7-9]. The literature guiding these recommendations typically report group norms which obscure the individual variation that occurs between subjects. However, over the course of the past two decades, evidence demonstrating the unexpectedly extensive inter-individual variation in response to similar training stimuli has accumulated exponentially [5, 6, 10-12]. The illustration of such wide-ranging variations in response conflicts with the traditional exercise prescription assumptions, questioning many of the traditional components of physical preparation.

Resolving the conceptual deficit between current evidence and conventional theory requires an understanding of the influences driving inter-individual response. These influences emanate from diverse academic domains, and the focus of this article is to identify those that interact to customise the inter-individual adaptive response. Furthermore, whilst past reviews have highlighted some of these influences [6], we add findings from the emerging field of epigenetics. Finally, we outline how these influences integrate, and suggest how an enhanced understanding of the broad range of factors influencing adaptive response can help contextualise the limits, and potential value of, emerging gene profiling technologies.

It is well established that, when subjected to the same stimulus, there is a wide variety in response within subjects. Large variations in muscle size (from -2% to +59%), changes in one-repetition maximum (1RM; 0% to +250%) and changes in maximum voluntary contraction (MVC; -32% to +149%) have been reported following 12-weeks of resistance training [10]. The same is true for aerobic capacity improvements, with the HERITAGE (HEalth, RIsk factors, exercise Training And GEnetics) family study finding a mean improvement in VO_2max of 384 mL O₂ min^-1 following 20-weeks’ training. However, some subjects saw no improvement, whilst a small number of subjects saw much larger improvements than the average, as high as 1100 mL O₂ min^-1 - almost four times the mean [5]. Other studies have reported large variations in response to high-intensity interval training [11], fat loss [13, 14], other health-related aspects including insulin sensitivity, blood pressure, and cholesterol levels [5], and response to ergogenic aids [15].

The individual response to exercise appears to be modality specific. Karavirta et al. [16] randomised 175 subjects into four groups; endurance training only, strength training only, concurrent strength and endurance training, and controls. All groups exhibited a large range in exercise response, with improvements in VO_2peak ranging from -10 to +60% in the endurance trained group, and MVC improvements ranging from -15 to +60% in the strength trained group. But it is the strength and endurance trained group where the crucial data lies; although some subjects saw a negative training response in either VO_2peak or MVC, no subject had a negative response in both. Additionally, no subject was in the highest quintile of improvement for both VO_2peak and MVC. Hautala et al. [17] found that when individuals were given both an endurance and resistance training block, improvements in VO_2max differed between modalities. Perhaps most promising was that those seeing the lowest VO₂max improvement following endurance training saw a greater VO_2max improvement following resistance training. This indicates there aren’t global responders and non-responders to exercise, merely responders and non-responders to specific exercise types. There are thousands of biochemical adaptations to exercise, and a multitude of different training modalities, making it unlikely that there are individuals who see no improvement at all following exercise [18]. This is not necessarily a consensus, however, with others finding that, whilst the inter-correlation in non-response to exercise between exercise modalities is low, it is not zero [19]. Exercise adaptation occurs through several separate pathways specific to each training modality. The lack of global non-responders suggests the driver of individual variation in exercise response could be down to variation within these pathways. This variation would likely occur due to genetic and non-genetic influences, which combine to create a unique adaptive outcome.

Outside of specific performance-enhancing adaptations following exercise, there is also a large range in terms of health improvements. Returning to HERITAGE, the mean improvement in insulin sensitivity within a sub-cohort was 10% [5]. However, this varied between subjects from large improvements, to no change, to a decrease in insulin sensitivity. Factors affecting this individual response included gender, race, and starting body weight [20]. Similarly, disease state can also modify improvements in insulin sensitivity following exercise training, with type-II diabetics and non-diabetics seeing improvements of differing magnitudes [21].

Even very complex traits, comprised of many factors, show large ranges in variability between subjects. Injury risk in sport is multifactorial, comprised of internal and external risk factors, along with an inciting event [22]. Even within this complex model, not all high-risk athletes will get injured, nor will low-risk athletes avoid injury [23]. The same is true when looking at response to a dietary supplement, such as vitamin D, with genotype, baseline serum 25-hydroxyvitain D (25(OH)D) levels and body mass index (BMI) all modifying increases in serum 25(OH)D levels following supplementation [24]. A large individual variation in the ergogenic effect of caffeine has also been widely reported [15, 25, 26].

Given that these large variations in exercise adaptation exist between subjects, it is necessary to identify and understand the causes of this variation. Here, we outline how proposed mechanisms interact to lead to the observed extensive inter-individual variation in exercise adaptation.

3.3. Summarising Gene-Environment Interactions

Having discussed the different genetic and environmental aspects that affect exercise response, it is worthwhile summarising these within a model. Fig. (1) shows the typical gene-environment model, where both genetic and environmental factors interact in an additive manner to determine the post-adaptation phenotype. As a simplified example, two individuals who are homozygous for the R allele of ACTN3 will have different phenotypes based on environmental factors. If subject A undertakes high-load resistance training, they will likely see good levels of muscle hypertrophy. If subject B is sedentary, then they won’t see hypertrophy, no matter how favourable their personal genotype.

Fig. (1). The Gene-Environment Model, with gene and environment interacting to create phenotype. In this example, ACTN3 is used to create a simplified overview.

However, as explored in environmental factors, there are a variety of environmental factors that affect training response. These have a complex relationship with genetic factors; they can affect genetic expression, but are also affected themselves by SNPs within specific genes. This allows us to create a more complex model, as per Fig. (2), which illustrates the increasing complexity.

Fig. (2). A more complex model illustrating the two-way relationship between various environmental and genetic factors to create the outcome, in this case exercise adaptation. Further complexity could be added to this model by showing the inter-relationship between the environmental factors; nutrition can affect psychological factors, for example.

3.4. Epigenetics

Having introduced genetics and environment, two aspects that we typically think combine to create the phenotype, we turn our attention to epigenetics. Epigenetics refers to changes in gene function that occur without a change in the nucleotide sequence [162]. These changes can be heritable, but also changeable over the course of time within an individual [163], influenced by both genetic and non-genetic factors. The three main epigenetic mechanisms are DNA methylation, histone modifications, and non-coding mRNA, and all act as a way for our environment, through factors discussed previously, to impact genetic expression.

3.4.1. DNA Methylation

The most extensively studied epigenetic mechanism, DNA methylation occurs through the addition of a methyl group to a cytosine base [164], making that section less accessible for translation [165]. This can be positive or negative depending on whether expression of that gene is desired; methylation of oncogenes and obesity-risk genes is likely positive, whilst methylation of tumor suppressor genes, and those driving exercise adaption is less ideal [166, 167]. The same stimulus can both increase and decrease in methylation in different genes [168]. For example, six-months of aerobic exercise lead to decreases in methylation (hypomethylation) of muscle genes, promoting adaptation [167], and increases in methylation (hypermethylation) in adipose tissue genes, potentially stimulating weight loss [169]. Similarly, PPARGC1A, a gene influencing mitochondrial biogenesis [170], exhibited increased methylation following nine-days of bed rest, and decreased methylation after four-weeks of re-training [171].

DNA methylation is modifiable within an individual; the methylation profiles of obese patients become more like lean subjects’ following a weight-loss intervention, for example [172]. The levels of methylation in response to the same stimulus may also change over time, with higher levels seen in elderly subjects’ post-exercise, possibly due to accumulation of aberrant methylation in these subjects that needs correcting with exercise [173]. Of the three epigenetic modifications detailed here, methylation is the most stable [164], with early life experiences – even those pre-birth – having a long-term effect on gene expression [174]. Methylation patterns can even be passed down generations, raising the possibility that methylation markers affecting elite athlete status and fitness may be partially inherited [168].

3.4.2. Histone Modification

Our DNA is coiled around histone proteins, giving it a specific shape. The epigenetic variation caused by histone modification occur via acetylation of this structural histone protein, changing its shape. This makes the DNA comparatively easier to read, increasing the expression of these genes [175]. Histone acetylation is controlled by histone acetyl-transferases (HAT), whilst histone deacetylases (HDAC) removes the acetyl group, reducing translation at that point [176]. In mice, the presence of a specific HDAC (HDAC5) can reduce the adaptations expected following exercise [177], illustrating how histone modifications might affect exercise response. In humans, HDAC5 levels are lower following training, confirming that these proteins play a role in exercise adaptation, although at present the causes of individual differences in HDAC5 levels are not clear [175]. Histone modifications are constantly in a state of flux, making them the most transient of the epigenetic changes [164].

3.4.3. Non-Coding RNA

RNA is typically used by the body as messenger RNA (mRNA), passing information from DNA to the ribosomes, where protein synthesis occurs. However, most RNA within the body is non-coding; instead, it regulates genetic expression or catalyses chemical reactions [178, 179, 180]. Within epigenetics, of interest is micro RNA (miRNA), molecules which exert control over mRNA, either by inhibiting translation or causing degradation before translation occurs [181]. This indicates miRNA could regulate gene transcription post-exercise, affecting adaptation. In subjects matched for diet, training history, age and body mass, a 12-week resistance training programme elicited adaptations of differing magnitude, partially mediated by specific miRNAs; levels of these miRNA were correlated with greater adaptations, including increases in strength [180]. miRNA has also been reported to influence aerobic exercise adaptation [180-183]. It’s not clear at present what factors affect circulating levels of miRNA, making it difficult to harness this knowledge at present.

At this point, we can update our model to include the impact of epigenetics on gene-environment interactions, as seen in Fig. (3).

Fig. (3). A simple model of gene-environment interactions, with the addition of epigenetics. In this model, environmental factors have been grouped together for simplicity. Here, these environmental changes alter genetic expression, although as we will see in the following sections, this is a complex relationship.

3.4.4. Genetic Influences on Epigenetic Modifications

So far, we’ve covered how epigenetic mechanisms allow for the environment to impact genetic expression, which is typically how epigenetics is viewed. However, genetic variation can also affect the efficiency of epigenetic modifications, bringing things full circle. This is most well established in terms of methylation, where several genes [184], including MTHFR [185], affect DNA methylation, in turn affecting epigenetic modifications post-exercise. Elite athletes have a greater number of polymorphisms across several genes that affect methylation status, resulting in a genetic predisposition to hypomethylate, and this lack of methylation potentially increases post-exercise muscle hypertrophy by increasing specific gene transcription [186, 187].

3.4.5. Environmental Influences on Epigenetic Modifications

Along with genetics, environmental influences such as nutrition can alter epigenetic modifications. For example, a high calorie diet appears to increase methylation of genes controlling metabolism, making metabolic dysfunction more likely [188]. As discussed, genes influence the efficiency of methylation, but also interact with environmental factors to control these changes, adding an extra layer of complexity. As an illustration, MTHFR encodes for an enzyme that coverts the folate derivate 5,10-methylenetetrahydrofolate (5,10MTHF) to 5-methylterahydrofolate (5MTHF), creating s-adenosylmethionine (SAM) – the agent for DNA methylation. Simply put, this pathway starts with folate, which is converted to intermediaries by MTHFR, with the availability of these intermediaries affecting methylation efficiency [189, 190]. The two common MTHFR SNPs, C677T and A1298C, influence the activity of the MTHFR enzyme. Focusing on C677T, T allele carriers typically have poorer conversion of folate, which influences methylation. When placed on a low folate diet (≈115μg/d) for seven weeks, subjects show a decrease in methylation. This effect is greater in TT genotypes, and was reversed after seven-week high folate diet (≈400μg/d) [190].

Exercise is another environmental influence that impacts epigenetic changes through alterations in gene silencing and expression [191]. The homeostatic stress caused by exercise drives epigenetic modifications [192], which in turn can lead to exercise adaptations by increasing translation and transcription of proteins involved in adaptive mechanisms, including AMPK and PGC-1a [193].

Environmental influences on epigenetic modification can play a big role in determining an individual’s phenotype; in individuals with the same genotype (monozygotic twins), differences in environment lead to different epigenetic changes [194], altering type-II diabetes risk, for example [195]. Alongside nutrition and exercise, other environmental factors that influence epigenetic modifications include psychological trauma [196, 197], which can be passed down generations [198], but also reversed [199]. Environmental toxins such as tobacco smoke, dietary polyphenols, alcohol and shift work all also impact epigenetic regulation [200].

Having reviewed the impact of both genetics and environmental factors on epigenetic modifications, we can add these factors into a final model, discussed in section 4.

As detailed earlier, genetics can clearly impact the magnitude of adaptation to exercise, both in single SNP/gene (e.g. ACTN3) and gene-combination (e.g. HERITAGE) models. Previously, we examined non-genetic aspects influencing this response, including nutritional status and training history. As an example, vitamin D status can impact performance gains [142], and vitamin D status itself is modified by sunlight exposure and supplementation [142], but also gene polymorphisms [24, 154]. The same is true for a number of other nutrients; polymorphisms within HFE [201], a gene that impacts iron status alongside dietary iron intake [202, 203], may also impact improvements in aerobic fitness following training [204]. It is clear, therefore, that genetic and non-genetic factors are linked. The same is true for acute environmental factors, such as a stressor, which we suggested might affect response to a single exercise bout. These acute factors are also influenced by genetic factors, such as a SNP in COMT, that modulates stress response [205]. They are also influenced by environmental factors such as previous trauma [206].

Having then introduced epigenetics, the mechanism through which environmental aspects influence genetic expression; we explored how genetic and non-genetic factors also influenced epigenetic modifications, further illustrating the complex relationship between all factors, requiring an update to the model proposed in Fig. (3). This culminates in a model illustrating how these factors interact, creating a unique outcome for each individual in response to a stimulus. This response is not stable, as the component factors themselves can be highly variable over time; just because an individual saw a performance improvement after one training programme doesn’t guarantee the same improvement following the same programme once more [207]. Fig. (4) illustrates this complex relationship.

Having discussed the main aspects that affect individual adaptation to exercise, it’s crucial to make this information usable to athletes. Competing at the highest level is a function of talent alongside optimal training – but how does an athlete know their training is optimal? Typically, this requires trial and error, which is costly in terms of both time, and, if the trial is ineffective, performance. Given that athletes only have a window of a few years to compete at their peak, time spent undertaking sub-optimal training can be damaging. Having more information on which to base decisions regarding training methodology would be very attractive to everyone involved in sport. Currently, most tests carried out on athletes are phenotypic, such as VO₂max and vitamin D tests. This testing has use, providing a snapshot of where the athlete is at a point in time and informing training requirements, but has minimal long-term predictive ability.

Fig. (4). The complex interaction between genes, environment and epigenetics on response to a stimulus, in this case a training programme. Environmental factors are contained within the bordered circle.

Given that a large proportion of inter-individual variation is down to genetic factors, testing for these factors holds promise. This could be single gene/SNP testing, or, more promisingly, larger scale testing such as whole genome sequencing. The cost of these tests has dropped in recent years, increasing accessibility [208]. This gives rise to the potential use of genetic tests to inform training programme design, which may have predictive ability [183, 209, 210]. Whilst single gene models might give some insight into exercise response [62, 211], adaptation to exercise is not determined by a single gene. Instead, groups of genes influence the various cellular pathways controlling adaptation [212]. By examining just one gene, such as ACTN3, we run the risk of ignoring the effects of these other genes. One way to overcome this is to use a multi-gene model, comprised of an algorithmic approach that allows for the evaluation of many gene polymorphisms.

One method used in this regard is the Total Genotype Score (TGS). This method has been used against retrospective data to improve identification of at-risk individuals for cardiovascular disease and type-II diabetes [213, 214]. Within the sports world, it has thus far been examined primarily as a potential tool for the discovery of elite athletes [30, 31, 215] although the consensus is that there is currently no predictive ability of genetics in the identification of elite athletes [216]. Pooled data from three independent aerobic training programmes – HERITAGE, DREW and STRRIDE – showed that those with a TGS of ≥19 had VO_2max improvements 2.7 times greater than those with a score of ≤9, although this was conducted post-hoc and not used to inform programme design [217]. Presently, the use of a TGS or other algorithm has not been widely utilised in regards to interventions to improve exercise response. One study used a TGS to retrospectively explain training response over the career of an athlete [218]. Another used a weighted algorithm to personalise an eight-week resistance training programme, with those doing genetically matched training demonstrating significantly greater improvements across tests of power and endurance than those doing genetically mismatched training. In addition, over 80% of subjects identified as high responders were from the matched group, whilst 82% of non-responders were from the mismatched group [209]. This suggests genetic testing might reduce non-response to exercise; something that will excite elite athletes, but which may also have public health connotations in the fight against obesity. Another method combined the use of RNA profiling with SNPs to create a molecular predictor of VO₂max response to aerobic training [183], although this has yet to be satisfactory replicated [88]. It is still early in this journey, with a far greater body of research required; nevertheless, it does appear that we are getting closer to being able to utilise this knowledge. Doing so will also require manipulation of training variables, such as exercise intensity, duration, volume, as well as nutritional interventions. It must be remembered that the use of genetic information can better inform these manipulations, but does not replace them.

Genetic testing is somewhat controversial [216, 219, 220], with the controversy comprised of various factors. One of these is the use of genetic testing for talent; certainly, there is no evidence that genetic testing can or should be used in this way [216]. The second is whether they have utility in terms of exercise modification. A recent consensus statement suggests they don’t [216], although no evidence is given within the statement to support this standpoint. It’s certainly true that, at present, only a small number of studies have looked at training modifications based on genetic information, but this number is expected to grow in the future, leading to the possibility that genetic information might have some use alongside other more traditional information sources [221, 222]. Finally, there are ethical aspects to consider and overcome. Should there be a minimum age for genetic tests? Can the results of a genetic test be placed in the correct context for an athlete? Who owns the genetic data, the athlete or the team? If an athlete refuses a genetic test, will they be discriminated against? What happens if a genetic test unearths a potential medical issue, such as increased Alzheimer’s disease risk? These questions, and others, will need to be answered before genetic testing can become widespread in sport. Finally, there needs to be assurances that genetic test results won’t be used for selection purposes, or any other discriminatory practices. If these ethical hurdles can be overcome, there is the potential to use genetic testing in exercise prescription and modification alongside other more traditional aspects.

Throughout the course of this review, we have explored some of the factors that modify the individual response to a stimulus, primarily exercise adaptation. We’ve seen how our environment can impact adaptation, through aspects such as sleep and nutrition, and we’ve also examined how epigenetic modifications allow communication between the environment and genetic expression. However, a constant theme throughout has been the influence of genetics on the response to a stimulus. Differences in genotype are responsible for a large amount of variation in exercise adaptation, but genetic factors also influence environmental aspects such as nutrition and epigenetic efficiency. Given that genetic factors are such a consistent and fundamental modulator of how someone responds to exercise, knowledge these factors within an individual would prove useful. For the first time, this knowledge is affordable and available through genetic testing, allowing athletes and coaches to have an idea of how they will respond, and to modify training to account for this. The information gained from a genetic test represents an additional piece of information to inform needs much like a vitamin D screen, heart rate variability for recovery, or a 1RM strength test. It is still early in the use of genetic testing for sports people, and a significant body of research is required to identify yet more SNPs involved in exercise adaptation, along with other areas of interest to athletes; injury risk, recovery speed, and the ergogenic effects of nutritional aids. However, research is starting to indicate the utility of these tests. Indeed, some sports teams have been using genetic information [223], but without any evidence-based practice. Given the apparent desire of high level sports people to utilize genetic information to inform programme design, the development of evidence-based guidelines is paramount, which of course means that further research on the potential use of genetic information in training response in required, particularly from a predictive standpoint. As such, further research should focus on:

• Replication of existing, and discovery of further SNPs that impact exercise adaptation.
• Examining the interplay between genes, environment, and epigenetic modifications on exercise adaptation.
• The development of evidence-based guidelines on the use of genetic assessments in sport, with particular reference to ethical considerations.

The ability to harness this information potentially represents a new dawn in understanding exercise adaptation, allowing athletes to better their quest to become faster, higher, and stronger.