Understanding Personalized Training Responses: Can Genetic Assessment Help?
REVIEW ARTICLE

Understanding Personalized Training Responses: Can Genetic Assessment Help?

The Open Sports Sciences Journal 30 Nov 2017 REVIEW ARTICLE DOI: 10.2174/1875399X01710010191

Abstract

Background:

Traditional exercise prescription is based on the assumption that exercise adaptation is predictable and standardised across individuals. However, evidence has emerged in the past two decades demonstrating that large inter-individual variation exists regarding the magnitude and direction of adaption following exercise.

Objective:

The aim of this paper was to discuss the key factors influencing this personalized response to exercise in a narrative review format.

Findings:

Genetic variation contributes significantly to the personalized training response, with specific polymorphisms associated with differences in exercise adaptation. These polymorphisms exist in a number of pathways controlling exercise adaptation. Environmental factors such as nutrition, psycho-emotional response, individual history and training programme design also modify the inter-individual adaptation following training. Within the emerging field of epigenetics, DNA methylation, histone modifications and non-coding RNA allow environmental and lifestyle factors to impact genetic expression. These epigenetic mechanisms are themselves modified by genetic and non-genetic factors, illustrating the complex interplay between variables in determining the adaptive response. Given that genetic factors are such a fundamental modulator of the inter-individual response to exercise, genetic testing may provide a useful and affordable addition to those looking to maximise exercise adaption, including elite athletes. However, there are ethical issues regarding the use of genetic tests, and further work is needed to provide evidence based guidelines for their use.

Conclusion:

There is considerable inter-individual variation in the adaptive response to exercise. Genetic assessments may provide an additional layer of information allowing personalization of training programmes to an individual’s unique biology.

Keywords: Inter-individual, Exercise, Adaptation, Genetics, Epigenetics, Psycho-emotional, Personalized.

1. INTRODUCTION

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.

2. INTER-INDIVIDUAL VARIATION IN RESPONSE TO TRAINING

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 VO2max 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 VO2peak 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 VO2peak or MVC, no subject had a negative response in both. Additionally, no subject was in the highest quintile of improvement for both VO2peak and MVC. Hautala et al. [17] found that when individuals were given both an endurance and resistance training block, improvements in VO2max differed between modalities. Perhaps most promising was that those seeing the lowest VO2max improvement following endurance training saw a greater VO2max 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].

3. POTENTIAL MECHANISMS DRIVING THE INDIVIDUAL RESPONSE

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.

3.1. Genetics

Following the completion of the Human Genome Project in 2003, genetic analysis has become increasingly affordable, making research into the effects of genes on fitness and performance more feasible. Knowledge of these genetic influences has progressed significantly in recent years, moving from the idea that all traits are determined by a single gene (which holds true in select disease states such as Cystic Fibrosis [27] and Huntington’s disease [28]), to more complex polygenic interactions. The “single gene as a magic bullet” philosophy has previously been present in sport [29], with some coaches believing that individual genes are responsible for athletic performance. However, no single gene has been discovered. Instead, we are faced with the reality that elite athletes possess many favourable alleles [30, 31] with no athlete possessing the perfect genetic profile for elite performance [32].

All traits, therefore, exist on a spectrum; from single gene traits at one end to complex polygenic traits at the other. Whilst it might be thought that complex traits can never be fully understood regarding their genetic component, research has identified candidate genes associated with highly complex traits such as intelligence [33], educational attainment [34], height [35], and chances of being an elite athlete [36]. Of course, these traits are also dependent on non-genetic factors, but there is an inherent genetic component within them. Returning to exercise adaptation, the heritable component differs from trait to trait. For example, the results of HERITAGE indicate approximately 50% of heterogeneity in VO2max improvement following training is determined by heritable factors [37], whilst muscle fibre type is 45% to 99.5% heritable [38, 39], and 52% of muscle strength phenotype is heritable [40]. Knowledge of genes affecting this response may allow for manipulation of training factors such as volume, intensity, frequency and rest-periods to improve exercise response. Indeed, recent research has argued whether true non-responders to specific exercise modalities exist, with increases in exercise intensity and frequency eliminating exercise non-response [41-43].

3.1.1. Gene Polymorphisms & Exercise Adaptation

At least 120 genetic markers are linked to elite athlete status [44], with approximately 10% of these replicated in at least three studies; yet more genes are implicated with exercise adaptation [45]. Elite athletes are a good start point in the search for candidate genes driving exercise response, as they represent a highly specialised cohort. For example, elite sprinters are likely very good at sprinting because they possess genotypes predisposing favourable adaptations following speed-power training. One such gene is ACTN3, which encodes for alpha-actinin-3, a protein that forms part of the Z-line in muscle fibres. A single nucleotide polymorphism (SNP) within ACTN3, known as R577X, arises from a C → T substitution, resulting in a premature stop codon (X) in place of arginine (R). Approximately 18% of individuals are homozygous for the X allele, causing a deficiency of alpha-actinin-3 [46]. Whilst this isn’t associated with any disease state, it does mean that XX genotypes tend to have a lower percentage of type-IIx muscle fibres [47]. The X allele is uncommon in elite speed-power athletes, and potentially more common in elite endurance athletes when compared to controls [48]. This indicates the XX genotype is unfavourable for elite power performance, but potentially has a beneficial effect on endurance performance. Subsequent research has confirmed the association between the R allele and power performance, although the link between the X allele and endurance is less clear [49]. Other replicated SNPs found to affect athletic performance include ACE I/D [50-52], PPARGC1A Gly482Ser [53-55], GABPB1 (rs7181866) [56, 57], BDKRB2 +9/-9 [58, 59], and HIF1A Pro582Ser [60, 61].

After identifying a relevant polymorphism, the next step is to elucidate how this polymorphism affects individual training response. With ACTN3, RR genotypes show greater improvements in peak power and strength than XX genotypes following resistance training in elderly populations [62, 63]. The mechanisms driving these differences are not fully understood. Increases in mTOR and p70S6k, stimulators of skeletal muscle hypertrophy, have been found to be greater in R allele carriers than XX genotypes following high-intensity exercise [64], and testosterone levels may be higher in RR genotypes [65]. R allele carriers also tend to have a higher percentage of type-II muscle fibres [66], potentially allowing greater hypertrophy following resistance training [67, 68]. These findings may go some way to explaining the differences in training responsiveness between ACTN3 genotypes. Again, similar research has shown a modifying effect of other polymorphisms on training response, including ACE I/D [69-71] and PPARGC1A Gly482Ser [72-74], as well as the influence of genetic variation on other traits, including injury risk [75-77] and exercise recovery [78, 79].

3.2. Environmental Factors

If heritable factors are responsible for a part of exercise adaptation, the obvious question to ask is - what is responsible for the other part? These non-genetic factors are often termed “environmental”, which we will define as non-genetic factors. Within this review we will divide them into four groups; individual history, programme design, psycho-emotional factors, and nutrition. These non-genetic factors can be both acute, affecting a single or small number of consecutive sessions, or chronic, affecting response to the training programme as a whole.

3.2.1. Individual History

A phenotype is the observable expression of an individual’s genotype, which is impacted by that person’s environment [80]. Within this paper, we can consider individuals to have either a highly-, normal-, or under-adaptive phenotype, influenced by their genotype (see Gene polymorphisms and exercise adaptation), but also environmental variables. One such variable is baseline fitness, which impacts recovery from exercise [81-83]. Another is previous training history, with trained individuals showing differences in adaptive mechanisms post-exercise compared to beginners [84], and subject age [85]. When looking at dietary interventions, diet history can modify responsiveness to interventions, with previous weight loss attempts potentially making future weight loss harder [86]. Finally, higher habitual physical activity can enhance the response to endurance training [87]. Within HERITAGE, correlates of VO2max improvements following training included baseline VO2max, age, gender, weight, ethnicity, and achievement of target workload [88]. Baseline phenotypes appear to influence separate traits to differently, comprising a smaller portion of VO2max improvements following exercise (11-16%) and a larger portion of blood pressure response following exercise (21-47%) [6].

3.2.2. Programme Design

Training programme design (exercise selection, frequency, duration, intensity, recovery times, repetition and set ranges, etc.) can also influence the magnitude of adaptation to training [67, 68, 89-93], as can time of day [94.95], such that two people with an identical genotype doing different training programmes would see a difference in phenotype. Indeed, increasing total exercise volume, frequency and intensity reduces, and perhaps eliminates, exercise non-response, suggesting that environmental influences can perhaps over-ride the genetic pre-disposition to exercise non-response [41-43, 96].

3.2.3. Psycho-Emotional Factors

In recent years, attention has turned to how the brain influences exercise performance and adaptation. Initially, this focused on fatigue, with both the Central Governor [97] and psychobiological [98] models proposed to explain the relationship between brain and fatigue. Following this, interest has built around understanding the relationship between brain and physiology, especially regarding exercise adaptation. Previous work has indicated that response to a stressor - including exercise, feelings of fatigue, and pain – is filtered through the brain’s emotional centres, which evaluate the stressor in terms of its threat [99]. Current perceptions suggest that biological adaptation, to an imposed or perceived stressor, is not regulated by the magnitude of that stressor, but by the nature of the stress response launched to remediate the challenge presented to the neuro-biological system [101]. The nature and magnitude of the stress response is hence governed primarily by the emotional resonance afforded the perceived threat presented by the stress-inducing event. This emotional interpretation subsequently initiates a neuro-chemical response, proportional in magnitude to the perceived threat presented by the stressor. This neurochemical response, in turn, launches the cascade of downstream bio-chemical responses which subsequently drive all peripheral adaptations [99-102]. From this, we can see that the magnitude of response to a stressor is not solely dependent on the stressor itself, but the emotional resonance attached to this stressor; this emotional response drives the bio-chemical and hormonal alterations, in turn driving all subsequent physiological and peripheral tissue adaptations.

This emotional response is complex, and is best summarised by Ganzel et al. [100] In their model, the authors describe the factors that mediate the emotional response, including prior context, such as previous traumatic experiences, evolved coping behaviours, and health. This prior context interacts with the current state of the organism, both in terms of emotional state (influencing prior mental health, which influences acute emotional response to a stressor) and, via the chemical changes that drive subsequent physical responses, physical changes that accompany chronic stress, such as increased cortisol (which influences prior physical health, itself a modifier to the acute response to a stressor). These factors combine to influence the weighting the emotional system places on an acute stressor, affecting the acute physical response.

By combining the work in this field, we can summarise that every stressor, including exercise, exerts a neurological, biological, psychological and emotional load depending on individual interpretation [99, 100, 103]. This means that what often feels solely like a physical response, such as fatigue, is mediated by perception, suggesting the psychological and biological responses to a stressor are irrevocably mutually entwined.

3.2.3.1. Factors Affecting Psycho-Emotional Response

The response to a stressor is altered by both environmental and genetic factors. These include lack of sleep, which impacts exercise recovery [104, 105], promoting the release of stress hormones [106]. This potentially leads to a loss of aerobic [107] and strength [108] ability, and increases the inflammatory response [109], altering training performance and hence adaptation. Sleep restriction, both acute and chronic, can alter the perception of a stressor [110], modifying the psycho-emotional response [111].

Stress interpretation is modified by heritable factors, including polymorphisms in genes such as COMT [112], BDNF [113, 114], and 5HTTLPR [113] that impact the stress response, altering exercise adaptation and performance [115, 116]. The microbiome, which is influenced by both environmental and genetic factors, also affects the stress response in athletes [117]. Finally, epigenetic modifications (see Epigenetics) impact stress interpretation pathways [118], explaining how childhood trauma influences adult stress behaviours [119].

The individual stress response also impacts the adaptive mechanisms following exercise. Psycho-emotional stress influences exercise adaptation by decreasing immunity and recovery [120], and increasing the risk of injury [121]. In addition, baseline stress has been correlated with VO2max improvements [122]. Given that the stress response is partially hormone led [123], and these hormonal changes can be fast-acting [124], the stress state of the subject at the time of exercise can modify the adaptive response both acutely and chronically [125]. As an example, subjects with lower stress scores show greater increases in both bench press and squat strength compared to subjects with higher stress scores [126]. Similarly, an athlete who has just argued with a spouse and has long-term financial worries is less likely to mount an optimal adaptive response than a content athlete [99].

The acute psycho-emotional response to exercise can cause variation in work-rate within that session, contributing to the inter-individual variation in exercise response [88]. Within-session work-rate is comprised of various factors, including residual fatigue, but also via psychological factors that impact within-session work via the psychobiological model [98]. Individual variation in perception of work-rate can lead to changes in exercise performance [127], and this perception of work rate is influenced by a myriad of factors [97]. Perception of effort also has a heritable component, which explains 35% of the variance in rating of perceived exertion (RPE) between subjects [128].

Finally, the placebo effect, expected outcomes, and previously-held beliefs alter the emotional evaluation of a stressor, modifying training performance and adaptation. An ever-increasing body of literature illustrates that a subject’s prior beliefs alter performance, including belief that they have taken caffeine [129], sodium bicarbonate [130], and doping agents [131-133]. Returning briefly to sleep, “placebo sleep” can improve cognitive function [134], again illustrating the power of belief. Given that expected beliefs can alter effort within a training session, whether a subject believes exercise is positive can affect the outcome of exercise- and nutritional-intervention trials [135, 136]. Emerging research seems to suggest that certain genotypes are more sensitive to expectancy, placebo and nocebo effects [137], again illustrating the consistent underlying influence of genetics on environmental factors.

3.2.4. Nutrition

An additional factor that influences exercise adaptation is nutrition. Macronutrient intake impacts both exercise performance and adaptation [138-140]. The same is true for micronutrients; for example, serum vitamin D levels are associated with muscle power and force, both acutely [141] and in response to a training programme [142]. Recently, attention has focused on individual variation within the gut microbiota, which impacts post-exercise recovery and mood states, altering adaptation [117, 143]. Finally, long-term high dose antioxidant use may blunt the adaptive response to exercise [144, 145], leading to the possibility that differences in dietary composition could contribute to the inter-individual variation in exercise response. Other nutritional factors modifying the acute physiological stress expected following training include short-term macronutrient intake [146], antioxidant intake [147, 148], and use of medications such as non-steroidal anti-inflammatory drugs (NSAIDS) [149-151].

These nutritional factors are influenced by genetic variation. The microbiota, for example, is influenced by host genetics [152]. Returning to the vitamin D example, polymorphisms in genes, including VDR, influence muscle strength [153], which in turn influences training response. VDR can also alter vitamin D requirements [154]. Vitamin D supplementation may also enhances improvements to a strength training programme [142], which begs the question - do non-responders to strength training not respond because of genetic factors, or is their response blunted due to vitamin D insufficiency (which in turn can be influenced by SNPs)? Given that nutrition impacts gene signaling post-exercise [155, 156], it’s easy to see how both genes and environment combine and interact to create the phenotype.

Finally, the use of ergogenic aids alters the performance level within individual training sessions, in turn affecting the long-term adaptation that accumulates over time. One such aid is caffeine, which has a clear, replicated ergogenic effect on exercise performance [157-159], the effects of which are modified by genetic variation [26, 160]. Another is creatine, which can affect intra-session recovery, allowing for a greater workload to be completed [161].

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.

4. A FINAL MODEL TO EXPLAIN THE CAUSES OF INTER-INDIVIDUAL VARIATION

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.

5. HARNESSING THIS KNOWLEDGE TO IMPROVE PERFORMANCE

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 VO2max 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.

CONCLUSIONS AND FUTURE DIRECTIONS

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.

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

Neither Craig Pickering nor John Kiely have any conflicts of interest pertaining from the publication of this manuscript.

ACKNOWLEDGEMENTS

Craig Pickering is an employee of DNAFit Ltd. He received no financial incentive for the production of this manuscript.

REFERENCES

1
Garber CE, Blissmer B, Deschenes MR, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med Sci Sports Exerc 2011; 43(7): 1334-59.
2
Kraemer WJ, Adams K, Cafarelli E, et al. American College of Sports Medicine position stand. Progression models in resistance training for healthy adults. Med Sci Sports Exerc 2002; 34(2): 364-80.
3
Hickey MS, Costill DL, McConell GK, Widrick JJ, Tanaka H. Day to day variation in time trial cycling performance. Int J Sports Med 1992; 13(6): 467-70.
4
Lamberts RP, Lambert MI. Day-to-day variation in heart rate at different levels of submaximal exertion: Implications for monitoring training. J Strength Cond Res 2009; 23(3): 1005-10.
5
Bouchard C, Rankinen T. Individual differences in response to regular physical activity. Med Sci Sports Exerc 2001; 33(6)(Suppl.): S446-51.
6
Mann TN, Lamberts RP, Lambert MI. High responders and low responders: Factors associated with individual variation in response to standardized training. Sports Med 2014; 44(8): 1113-24.
7
Matveyev LP. Fundamentals of Sport Training 1981.
8
Issurin VB. New horizons for the methodology and physiology of training periodization. Sports Med 2010; 40(3): 189-206.
9
Verkhoshansky V. Organisation of the training process. New Stud Athl 1998; 13(3): 21-31.
10
Hubal MJ, Gordish-Dressman H, Thompson PD, et al. Variability in muscle size and strength gain after unilateral resistance training. Med Sci Sports Exerc 2005; 37(6): 964-72.
11
Astorino TA, Schubert MM. Individual responses to completion of short-term and chronic interval training: A retrospective study. PLoS One 2014; 9(5): e97638.
12
Bouchard C, Daw EW, Rice T, et al. Familial resemblance for VO2max in the sedentary state: The HERITAGE family study. Med Sci Sports Exerc 1998; 30(2): 252-8.
13
Barbeau P, Gutin B, Litaker M, Owens S, Riggs S, Okuyama T. Correlates of individual differences in body-composition changes resulting from physical training in obese children. Am J Clin Nutr 1999; 69(4): 705-11.
14
Barwell ND, Malkova D, Leggate M, Gill JM. Individual responsiveness to exercise-induced fat loss is associated with change in resting substrate utilization. Metabolism 2009; 58(9): 1320-8.
15
Jenkins NT, Trilk JL, Singhal A, O’Connor PJ, Cureton KJ. Ergogenic effects of low doses of caffeine on cycling performance. Int J Sport Nutr Exerc Metab 2008; 18(3): 328-42.
16
Karavirta L, Häkkinen K, Kauhanen A, et al. Individual responses to combined endurance and strength training in older adults. Med Sci Sports Exerc 2011; 43(3): 484-90.
17
Hautala AJ, Kiviniemi AM, Mäkikallio TH, et al. Individual differences in the responses to endurance and resistance training. Eur J Appl Physiol 2006; 96(5): 535-42.
18
Booth FW, Laye MJ. The future: Genes, physical activity and health. Acta Physiol (Oxf) 2010; 199(4): 549-56.
19
Timmons JA. Variability in training-induced skeletal muscle adaptation. J Appl Physiol 2011; 110(3): 846-53.
20
Boulé NG, Weisnagel SJ, Lakka TA, et al. Effects of exercise training on glucose homeostasis: The HERITAGE Family Study. Diabetes Care 2005; 28(1): 108-14.
21
Holloszy JO, Schultz J, Kusnierkiewicz J, Hagberg JM, Ehsani AA. Effects of exercise on glucose tolerance and insulin resistance. Brief review and some preliminary results. Acta Med Scand Suppl 1986; 711(S711): 55-65.
22
Bahr R, Krosshaug T. Understanding injury mechanisms: A key component of preventing injuries in sport. Br J Sports Med 2005; 39(6): 324-9.
23
Bahr R. Why screening tests to predict injury do not work-and probably never will…: A critical review. Br J Sports Med 2016; 50(13): 776-80.
24
Didriksen A, Grimnes G, Hutchinson MS, et al. The serum 25-hydroxyvitamin D response to vitamin D supplementation is related to genetic factors, BMI, and baseline levels. Eur J Endocrinol 2013; 169(5): 559-67.
25
Ganio MS, Klau JF, Casa DJ, Armstrong LE, Maresh CM. Effect of caffeine on sport-specific endurance performance: A systematic review. J Strength Cond Res 2009; 23(1): 315-24.
26
Womack CJ, Saunders MJ, Bechtel MK, et al. The influence of a CYP1A2 polymorphism on the ergogenic effects of caffeine. J Int Soc Sports Nutr 2012; 9(1): 7.
27
Riordan JR, Rommens JM, Kerem B, et al. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 1989; 245(4922): 1066-73.
28
Walker FO. Huntington’s disease. Lancet 2007; 369(9557): 218-28.
29
Davids K, Baker J. Genes, environment and sport performance: Why the nature-nurture dualism is no longer relevant. Sports Med 2007; 37(11): 961-80.
30
Ruiz JR, Gómez-Gallego F, Santiago C, et al. Is there an optimum endurance polygenic profile? J Physiol 2009; 587(Pt 7): 1527-34.
31
Santiago C, Ruiz JR, Muniesa CA, González-Freire M, Gómez-Gallego F, Lucia A. Does the polygenic profile determine the potential for becoming a world-class athlete? Insights from the sport of rowing. Scand J Med Sci Sports 2010; 20(1): e188-94.
32
Hughes DC, Day SH, Ahmetov II, Williams AG. Genetics of muscle strength and power: Polygenic profile similarity limits skeletal muscle performance. J Sports Sci 2011; 29(13): 1425-34.
33
Davies G, Tenesa A, Payton A, et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry 2011; 16(10): 996-1005.
34
Rietveld CA, Medland SE, Derringer J, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 2013; 340(6139): 1467-71.
35
Silventoinen K, Sammalisto S, Perola M, et al. Heritability of adult body height: A comparative study of twin cohorts in eight countries. Twin Res 2003; 6(5): 399-408.
36
De Moor MH, Spector TD, Cherkas LF, et al. Genome-wide linkage scan for athlete status in 700 British female DZ twin pairs. Twin Res Hum Genet 2007; 10(6): 812-20.
37
Bouchard C, Daw EW, Rice T, et al. Familial resemblance for VO2max in the sedentary state: The HERITAGE family study. Med Sci Sports Exerc 1998; 30(2): 252-8.
38
Komi PV, Viitasalo JH, Havu M, Thorstensson A, Sjödin B, Karlsson J. Skeletal muscle fibres and muscle enzyme activities in monozygous and dizygous twins of both sexes. Acta Physiol Scand 1977; 100(4): 385-92.
39
Simoneau JA, Bouchard C. Genetic determinism of fiber type proportion in human skeletal muscle. FASEB J 1995; 9(11): 1091-5.
40
Zempo H, Miyamoto-Mikami E, Kikuchi N, Fuku N, Miyachi M, Murakami H. Heritability estimates of muscle strength-related phenotypes: A systematic review and meta-analysis. Scand J Med Sci Sports 2016.
41
Scharhag-Rosenberger F, Walitzek S, Kindermann W, Meyer T. Differences in adaptations to 1 year of aerobic endurance training: individual patterns of nonresponse. Scand J Med Sci Sports 2012; 22(1): 113-8.
42
Ross R, de Lannoy L, Stotz PJ. Separate effects of intensity and amount of exercise on interindividual cardiorespiratory fitness response. Mayo Clin Proc 2015; 90(11): 1506-14.
43
Montero D, Lundby C. Refuting the myth of non‐response to exercise training: ‘non-responders’ do respond to higher dose of training. J Physiol
44
Ahmetov II, Fedotovskaya ON. Current progress in sports genomics. Adv Clin Chem 2015; 70: 247-314.
45
Bray MS, Hagberg JM, Pérusse L, et al. The human gene map for performance and health-related fitness phenotypes: The 2006-2007 update. Med Sci Sports Exerc 2009; 41(1): 35-73.
46
North KN, Yang N, Wattanasirichaigoon D, Mills M, Easteal S, Beggs AH. A common nonsense mutation results in alpha-actinin-3 deficiency in the general population. Nat Genet 1999; 21(4): 353-4.
47
Vincent B, De Bock K, Ramaekers M, et al. ACTN3 (R577X) genotype is associated with fiber type distribution. Physiol Genomics 2007; 32(1): 58-63.
48
Yang N, MacArthur DG, Gulbin JP, et al. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 2003; 73(3): 627-31.
49
Ma F, Yang Y, Li X, et al. The association of sport performance with ACE and ACTN3 genetic polymorphisms: A systematic review and meta-analysis. PLoS One 2013; 8(1): e54685.
50
Collins M, Xenophontos SL, Cariolou MA, et al. The ACE gene and endurance performance during the South African Ironman Triathlons. Med Sci Sports Exerc 2004; 36(8): 1314-20.
51
Gayagay G, Yu B, Hambly B, et al. Elite endurance athletes and the ACE I allele--the role of genes in athletic performance. Hum Genet 1998; 103(1): 48-50.
52
Nazarov IB, Woods DR, Montgomery HE, et al. The angiotensin converting enzyme I/D polymorphism in Russian athletes. Eur J Hum Genet 2001; 9(10): 797-801.
53
Eynon N, Meckel Y, Alves AJ, et al. Is there an interaction between PPARD T294C and PPARGC1A Gly482Ser polymorphisms and human endurance performance? Exp Physiol 2009; 94(11): 1147-52.
54
Lucia A, Gómez-Gallego F, Barroso I, et al. PPARGC1A genotype (Gly482Ser) predicts exceptional endurance capacity in European men. J Appl Physiol 2005; 99(1): 344-8.
55
Maciejewska A, Sawczuk M, Cieszczyk P, Mozhayskaya IA, Ahmetov II. The PPARGC1A gene Gly482Ser in Polish and Russian athletes. J Sports Sci 2012; 30(1): 101-13.
56
Eynon N, Sagiv M, Meckel Y, et al. NRF2 intron 3 A/G polymorphism is associated with endurance athletes’ status. J Appl Physiol 2009; 107(1): 76-9.
57
Maciejewska-Karłowska A, Leońska-Duniec A, Cięszczyk P, et al. The GABPB1 gene A/G polymorphism in Polish rowers. J Hum Kinet 2012; 31: 115-20.
58
Williams AG, Dhamrait SS, Wootton PT, et al. Bradykinin receptor gene variant and human physical performance. J Appl Physiol 2004; 96(3): 938-42.
59
Saunders CJ, Xenophontos SL, Cariolou MA, Anastassiades LC, Noakes TD, Collins M. The bradykinin β 2 receptor (BDKRB2) and endothelial nitric oxide synthase 3 (NOS3) genes and endurance performance during Ironman Triathlons. Hum Mol Genet 2006; 15(6): 979-87.
60
Döring F, Onur S, Fischer A, et al. A common haplotype and the Pro582Ser polymorphism of the hypoxia-inducible factor-1α (HIF1A) gene in elite endurance athletes. J Appl Physiol 2010; 108(6): 1497-500.
61
Gabbasov RT, Arkhipova AA, Borisova AV, et al. The HIF1A gene Pro582Ser polymorphism in Russian strength athletes. J Strength Cond Res 2013; 27(8): 2055-8.
62
Delmonico MJ, Kostek MC, Doldo NA, et al. Alpha-actinin-3 (ACTN3) R577X polymorphism influences knee extensor peak power response to strength training in older men and women. J Gerontol A Biol Sci Med Sci 2007; 62(2): 206-12.
63
Pereira A, Costa AM, Izquierdo M, Silva AJ, Bastos E, Marques MC. ACE I/D and ACTN3 R/X polymorphisms as potential factors in modulating exercise-related phenotypes in older women in response to a muscle power training stimuli. Age (Dordr) 2013; 35(5): 1949-59.
64
Norman B, Esbjörnsson M, Rundqvist H, Österlund T, Glenmark B, Jansson E. ACTN3 genotype and modulation of skeletal muscle response to exercise in human subjects. J Appl Physiol 2014; 116(9): 1197-203.
65
Ahmetov II, Donnikov AE, Trofimov DY. Actn3 genotype is associated with testosterone levels of athletes. Biol Sport 2014; 31(2): 105-8.
66
Ahmetov II, Vinogradova OL, Williams AG. Gene polymorphisms and fiber-type composition of human skeletal muscle. Int J Sport Nutr Exerc Metab 2012; 22(4): 292-303.
67
Campos GE, Luecke TJ, Wendeln HK, et al. Muscular adaptations in response to three different resistance-training regimens: Specificity of repetition maximum training zones. Eur J Appl Physiol 2002; 88(1-2): 50-60.
68
Fry AC. The role of resistance exercise intensity on muscle fibre adaptations. Sports Med 2004; 34(10): 663-79.
69
Cam S, Colakoglu M, Colakoglu S, Sekuri C, Berdeli A. ACE I/D gene polymorphism and aerobic endurance development in response to training in a non-elite female cohort. J Sports Med Phys Fitness 2007; 47(2): 234-8.
70
Folland J, Leach B, Little T, et al. Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload. Exp Physiol 2000; 85(5): 575-9.
71
Giaccaglia V, Nicklas B, Kritchevsky S, et al. Interaction between angiotensin converting enzyme insertion/deletion genotype and exercise training on knee extensor strength in older individuals. Int J Sports Med 2008; 29(1): 40-4.
72
Ring-Dimitriou S, Kedenko L, Kedenko I, et al. Does genetic variation in PPARGC1A affect exercise-induced changes in ventilatory thresholds and metabolic syndrome. J Exerc Physiol Online 2014; 17: 1-18.
73
Stefan N, Thamer C, Staiger H, et al. Genetic variations in PPARD and PPARGC1A determine mitochondrial function and change in aerobic physical fitness and insulin sensitivity during lifestyle intervention. J Clin Endocrinol Metab 2007; 92(5): 1827-33.
74
Steinbacher P, Feichtinger RG, Kedenko L, et al. The single nucleotide polymorphism Gly482Ser in the PGC-1α gene impairs exercise-induced slow-twitch muscle fibre transformation in humans. PLoS One 2015; 10(4): e0123881.
75
Mokone GG, Schwellnus MP, Noakes TD, Collins M. The COL5A1 gene and Achilles tendon pathology. Scand J Med Sci Sports 2006; 16(1): 19-26.
76
Posthumus M, September AV, Schwellnus MP, Collins M. Investigation of the Sp1-binding site polymorphism within the COL1A1 gene in participants with Achilles tendon injuries and controls. J Sci Med Sport 2009; 12(1): 184-9.
77
September AV, Cook J, Handley CJ, van der Merwe L, Schwellnus MP, Collins M. Variants within the COL5A1 gene are associated with Achilles tendinopathy in two populations. Br J Sports Med 2009; 43(5): 357-65.
78
Baumert P, Lake MJ, Stewart CE, Drust B, Erskine RM. Genetic variation and exercise-induced muscle damage: Implications for athletic performance, injury and ageing. Eur J Appl Physiol 2016; 116(9): 1595-625.
79
Yamin C, Duarte JA, Oliveira JM, et al. IL6 (-174) and TNFA (-308) promoter polymorphisms are associated with systemic creatine kinase response to eccentric exercise. Eur J Appl Physiol 2008; 104(3): 579-86.
80
Winawer MR. Phenotype definition in epilepsy. Epilepsy Behav 2006; 8(3): 462-76.
81
Hagberg JM, Hickson RC, Ehsani AA, Holloszy JO. Faster adjustment to and recovery from submaximal exercise in the trained state. J Appl Physiol 1980; 48(2): 218-24.
82
Short KR, Sedlock DA. Excess postexercise oxygen consumption and recovery rate in trained and untrained subjects. J Appl Physiol 1997; 83(1): 153-9.
83
Tomlin DL, Wenger HA. The relationship between aerobic fitness and recovery from high intensity intermittent exercise. Sports Med 2001; 31(1): 1-11.
84
Coffey VG, Shield A, Canny BJ, Carey KA, Cameron-Smith D, Hawley JA. Interaction of contractile activity and training history on mRNA abundance in skeletal muscle from trained athletes. Am J Physiol Endocrinol Metab 2006; 290(5): E849-55.
85
Kosek DJ, Kim JS, Petrella JK, Cross JM, Bamman MM. Efficacy of 3 days/wk resistance training on myofiber hypertrophy and myogenic mechanisms in young vs. older adults. J Appl Physiol 2006; 101(2): 531-44.
86
Higginson AD, McNamara JM. An adaptive response to uncertainty can lead to weight gain during dieting attempts. Evol Med Public Health 2016; 2016(1): 369-80.
87
Hautala A, Martinmaki K, Kiviniemi A, et al. Effects of habitual physical activity on response to endurance training. J Sports Sci 2012; 30(6): 563-9.
88
Sarzynski MA, Ghosh S, Bouchard C. Genomic and transcriptomic predictors of response levels to endurance exercise training. J Physiol 2017; 595(9): 2931-9.
89
Wilson GJ, Newton RU, Murphy AJ, Humphries BJ. The optimal training load for the development of dynamic athletic performance. Med Sci Sports Exerc 1993; 25(11): 1279-86.
90
Contreras B, Vigotsky AD, Schoenfeld BJ, et al. Effects of a six-week hip thrust versus front squat resistance training program on performance in adolescent males: A randomized-controlled trial. J Strength Cond Res 2017; 31(4): 999-1008.
91
Rossi FE, Schoenfeld BJ, Ocetnik S, et al. Strength, body composition, and functional outcomes in the squat versus leg press exercises. J Sports Med Phys Fitness 2016; 13. Epub ahead of print
92
Schoenfeld BJ, Contreras B, Vigotsky AD, Peterson M. Differential effects of heavy versus moderate loads on measures of strength and hypertrophy in resistance-trained men. J Sports Sci Med 2016; 15(4): 715-22.
93
Schoenfeld BJ, Ogborn D, Krieger JW. The dose–response relationship between resistance training volume and muscle hypertrophy: Are there really still any doubts? J Sports Sci 2016; 2: 1-3.
94
Facer-Childs E, Brandstaetter R. The impact of circadian phenotype and time since awakening on diurnal performance in athletes. Curr Biol 2015; 25(4): 518-22.
95
Ammar A, Chtourou H, Souissi N. Effect of time-of-day on biochemical markers in response to physical exercise. J Strength Cond Res 2017; 31(1): 272-82.
96
Sisson SB, Katzmarzyk PT, Earnest CP, Bouchard C, Blair SN, Church TS. Volume of exercise and fitness nonresponse in sedentary, postmenopausal women. Med Sci Sports Exerc 2009; 41(3): 539-45.
97
Noakes TD. Fatigue is a brain-derived emotion that regulates the exercise behavior to ensure the protection of whole body homeostasis. Front Physiol 2012; 3: 82.
98
Smirmaul BP, Dantas JL, Nakamura FY, Pereira G. The psychobiological model: A new explanation to intensity regulation and (in) tolerance in endurance exercise. Rev Bras Educ Fís Esporte 2013; 27(2): 333-40.
99
Kiely J. A New Understanding of Stress and the Implications for Our Cultural Training Paradigm 2012. Available at: https://drive. google.com/file/d/0B9q1SjLGpRXnWnNuVGdtSDktdEk/view
100
Ganzel BL, Morris PA, Wethington E. Allostasis and the human brain: Integrating models of stress from the social and life sciences. Psychol Rev 2010; 117(1): 134-74.
101
Viru A, Viru M. Cortisol-essential adaptation hormone in exercise. Int J Sports Med 2004; 25(6): 461-4.
102
Kraemer WJ, Ratamess NA. Hormonal responses and adaptations to resistance exercise and training. Sports Med 2005; 35(4): 339-61.
103
McEwen BS. Stress, adaptation, and disease. Allostasis and allostatic load. Ann N Y Acad Sci 1998; 840: 33-44.
104
Dattilo M, Antunes HK, Medeiros A, et al. Sleep and muscle recovery: Endocrinological and molecular basis for a new and promising hypothesis. Med Hypotheses 2011; 77(2): 220-2.
105
Leeder J, Glaister M, Pizzoferro K, Dawson J, Pedlar C. Sleep duration and quality in elite athletes measured using wristwatch actigraphy. J Sports Sci 2012; 30(6): 541-5.
106
Dattilo M, Antunes HK, Medeiros A, et al. Paradoxical sleep deprivation induces muscle atrophy. Muscle Nerve 2012; 45(3): 431-3.
107
Azboy O, Kaygisiz Z. Effects of sleep deprivation on cardiorespiratory functions of the runners and volleyball players during rest and exercise. Acta Physiol Hung 2009; 96(1): 29-36.
108
Souissi N, Souissi M, Souissi H, et al. Effect of time of day and partial sleep deprivation on short-term, high-power output. Chronobiol Int 2008; 25(6): 1062-76.
109
Heffner KL, Ng HM, Suhr JA, et al. Sleep disturbance and older adults’ inflammatory responses to acute stress. Am J Geriatr Psychiatry 2012; 20(9): 744-52.
110
Minkel JD, Banks S, Htaik O, et al. Sleep deprivation and stressors: Evidence for elevated negative affect in response to mild stressors when sleep deprived. Emotion 2012; 12(5): 1015-20.
111
Åkerstedt T, Orsini N, Petersen H, Axelsson J, Lekander M, Kecklund G. Predicting sleep quality from stress and prior sleep-a study of day-to-day covariation across six weeks. Sleep Med 2012; 13(6): 674-9.
112
Clark R, DeYoung CG, Sponheim SR, et al. Predicting post-traumatic stress disorder in veterans: interaction of traumatic load with COMT gene variation. J Psychiatr Res 2013; 47(12): 1849-56.
113
Clasen PC, Wells TT, Knopik VS, McGeary JE, Beevers CG. 5-HTTLPR and BDNF Val66Met polymorphisms moderate effects of stress on rumination. Genes Brain Behav 2011; 10(7): 740-6.
114
Colzato LS, Van der Does AJ, Kouwenhoven C, Elzinga BM, Hommel B. BDNF Val66Met polymorphism is associated with higher anticipatory cortisol stress response, anxiety, and alcohol consumption in healthy adults. Psychoneuroendocrinology 2011; 36(10): 1562-9.
115
Petito A, Altamura M, Iuso S, et al. The relationship between personality traits, the 5HTT polymorphisms, and the occurrence of anxiety and depressive symptoms in elite athletes. PLoS One 2016; 11(6): e0156601.
116
Sanhueza JA, Zambrano T, Bahamondes-Avila C, Salazar LA. Association of anxiety-related polymorphisms with sports performance in chilean long distance triathletes: A pilot study. J Sports Sci Med 2016; 15(4): 554-61.
117
Clark A, Mach N. Exercise-induced stress behavior, gut-microbiota-brain axis and diet: A systematic review for athletes. J Int Soc Sports Nutr 2016; 13(1): 43.
118
Nestler EJ. Epigenetics: Stress makes its molecular mark. Nature 2012; 490(7419): 171-2.
119
Heim C, Binder EB. Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Exp Neurol 2012; 233(1): 102-11.
120
Clow A, Hucklebridge F. The impact of psychological stress on immune function in the athletic population. Exerc Immunol Rev 2001; 7: 5-17.
121
Mann JB, Bryant KR, Johnstone B, Ivey PA, Sayers SP. Effect of physical and academic stress on illness and injury in division 1 college football players. J Strength Cond Res 2016; 30(1): 20-5.
122
Ruuska PS, Hautala AJ, Kiviniemi AM, Mäkikallio TH, Tulppo MP. Self-rated mental stress and exercise training response in healthy subjects. Front Physiol 2012; 3: 51.
123
Huang YJ, Chen MT, Fang CL, Lee WC, Yang SC, Kuo CH. A possible link between exercise-training adaptation and dehydroepiandrosterone sulfate- an oldest-old female study. Int J Med Sci 2006; 3(4): 141-7.
124
Cook CJ, Crewther BT. Changes in salivary testosterone concentrations and subsequent voluntary squat performance following the presentation of short video clips. Horm Behav 2012; 61(1): 17-22.
125
Main LC, Landers GJ, Grove JR, Dawson B, Goodman C. Training patterns and negative health outcomes in triathlon: longitudinal observations across a full competitive season. J Sports Med Phys Fitness 2010; 50(4): 475-85.
126
Bartholomew JB, Stults-Kolehmainen MA, Elrod CC, Todd JS. Strength gains after resistance training: the effect of stressful, negative life events. J Strength Cond Res 2008; 22(4): 1215-21.
127
Marcora SM, Staiano W. The limit to exercise tolerance in humans: Mind over muscle? Eur J Appl Physiol 2010; 109(4): 763-70.
128
Schutte NM, Nederend I, Hudziak JJ, Bartels M, de Geus EJ. Heritability of the affective response to exercise and its correlation to exercise behavior
129
Saunders B, de Oliveira LF, da Silva RP, et al. Placebo in sports nutrition: A proof-of-principle study involving caffeine supplementation. Scand J Med Sci Sports 2017; 27(11): 1240-7.
130
McClung M, Collins D. “Because I know it will!”: Placebo effects of an ergogenic aid on athletic performance. J Sport Exerc Psychol 2007; 29(3): 382-94.
131
Ariel G, Saville W. Anabolic steroids: The physiological effects of placebos. Med Sci Sports Exerc 1972; 4(2): 124-6.
132
Maganaris CN, Collins D, Sharp M. Expectancy effects and strength training: Do steroids make a difference? Sport Psychol 2000; 14(3): 272-8.
133
Ross R, Gray CM, Gill JM. The effects of an injected placebo on endurance running performance. Med Sci Sports Exerc 2015; 47(8): 1672-81.
134
Draganich C, Erdal K. Placebo sleep affects cognitive functioning. J Exp Psychol Learn Mem Cogn 2014; 40(3): 857-64.
135
Beedie CJ, Foad AJ. The placebo effect in sports performance: A brief review. Sports Med 2009; 39(4): 313-29.
136
Mothes H, Leukel C, Jo HG, Seelig H, Schmidt S, Fuchs R. Expectations affect psychological and neurophysiological benefits even after a single bout of exercise. J Behav Med 2016; 9: 1-14.
137
Hall KT, Loscalzo J, Kaptchuk TJ. Genetics and the placebo effect: The placebome. Trends Mol Med 2015; 21(5): 285-94.
138
Bergström J, Hermansen L, Hultman E, Saltin B. Diet, muscle glycogen and physical performance. Acta Physiol Scand 1967; 71(2): 140-50.
139
Bartlett JD, Hawley JA, Morton JP. Carbohydrate availability and exercise training adaptation: Too much of a good thing? Eur J Sport Sci 2015; 15(1): 3-12.
140
Hammond KM, Impey SG, Currell K, et al. Postexercise high-fat feeding suppresses p70S6K1 activity in human skeletal muscle. Med Sci Sports Exerc 2016; 48(11): 2108.
141
Ward KA, Das G, Berry JL, et al. Vitamin D status and muscle function in post-menarchal adolescent girls. J Clin Endocrinol Metab 2009; 94(2): 559-63.
142
Close GL, Russell J, Cobley JN, et al. Assessment of vitamin D concentration in non-supplemented professional athletes and healthy adults during the winter months in the UK: Implications for skeletal muscle function. J Sports Sci 2013; 31(4): 344-53.
143
Mach N, Fuster-Botella D. Endurance exercise and gut microbiota: A review. J Sport Health Sci
144
Ristow M, Zarse K, Oberbach A, et al. Antioxidants prevent health-promoting effects of physical exercise in humans. Proc Natl Acad Sci USA 2009; 106(21): 8665-70.
145
Draeger CL, Naves A, Marques N, et al. Controversies of antioxidant vitamins supplementation in exercise: Ergogenic or ergolytic effects in humans? J Int Soc Sports Nutr 2014; 11(1): 4.
146
Hawley JA, Burke LM, Phillips SM, Spriet LL. Nutritional modulation of training-induced skeletal muscle adaptations. J Appl Physiol 2011; 110(3): 834-45.
147
Braakhuis AJ, Hopkins WG, Lowe TE. Effects of dietary antioxidants on training and performance in female runners. Eur J Sport Sci 2014; 14(2): 160-8.
148
Braakhuis AJ, Hopkins WG. Impact of dietary antioxidants on sport performance: A review. Sports Med 2015; 45(7): 939-55.
149
Trappe TA, White F, Lambert CP, Cesar D, Hellerstein M, Evans WJ. Effect of ibuprofen and acetaminophen on postexercise muscle protein synthesis. Am J Physiol Endocrinol Metab 2002; 282(3): E551-6.
150
Mackey AL, Kjaer M, Dandanell S, et al. The influence of anti-inflammatory medication on exercise-induced myogenic precursor cell responses in humans. J Appl Physiol 2007; 103(2): 425-31.
151
Mikkelsen UR, Langberg H, Helmark IC, et al. Local NSAID infusion inhibits satellite cell proliferation in human skeletal muscle after eccentric exercise. J Appl Physiol 2009; 107(5): 1600-11.
152
Bonder MJ, Kurilshikov A, Tigchelaar EF, et al. The effect of host genetics on the gut microbiome. Nat Genet 2016; 48(11): 1407-12.
153
Grundberg E, Brändström H, Ribom EL, Ljunggren O, Mallmin H, Kindmark A. Genetic variation in the human vitamin D receptor is associated with muscle strength, fat mass and body weight in Swedish women. Eur J Endocrinol 2004; 150(3): 323-8.
154
Graafmans WC, Lips P, Ooms ME, van Leeuwen JP, Pols HA, Uitterlinden AG. The effect of vitamin D supplementation on the bone mineral density of the femoral neck is associated with vitamin D receptor genotype. J Bone Miner Res 1997; 12(8): 1241-5.
155
Arkinstall MJ, Tunstall RJ, Cameron-Smith D, Hawley JA. Regulation of metabolic genes in human skeletal muscle by short-term exercise and diet manipulation. Am J Physiol Endocrinol Metab 2004; 287(1): E25-31.
156
Churchley EG, Coffey VG, Pedersen DJ, et al. Influence of preexercise muscle glycogen content on transcriptional activity of metabolic and myogenic genes in well-trained humans. J Appl Physiol 2007; 102(4): 1604-11.
157
Graham TE. Caffeine and exercise: metabolism, endurance and performance. Sports Med 2001; 31(11): 785-807.
158
Doherty M, Smith PM. Effects of caffeine ingestion on exercise testing: A meta-analysis. Int J Sport Nutr Exerc Metab 2004; 14(6): 626-46.
159
Astorino TA, Roberson DW. Efficacy of acute caffeine ingestion for short-term high-intensity exercise performance: A systematic review. J Strength Cond Res 2010; 24(1): 257-65.
160
Loy BD, O’Connor PJ, Lindheimer JB, Covert SF. Caffeine is ergogenic for Adenosine A2A Receptor gene (ADORA2A) T allele homozygotes: A pilot study. J Caffeine Res 2015; 5(2): 73-81.
161
Mujika I, Padilla S. Creatine supplementation as an ergogenic aid for sports performance in highly trained athletes: A critical review. Int J Sports Med 1997; 18(7): 491-6.
162
Ling C, Groop L. Epigenetics: A molecular link between environmental factors and type 2 diabetes. Diabetes 2009; 58(12): 2718-25.
163
Moran CN, Pitsiladis YP. Tour de France Champions born or made: where do we take the genetics of performance? J Sports Sci 2016; 6: 1-9.
164
Ehlert T, Simon P, Moser DA. Epigenetics in sports. Sports Med 2013; 43(2): 93-110.
165
Rottach A, Leonhardt H, Spada F. DNA methylation-mediated epigenetic control. J Cell Biochem 2009; 108(1): 43-51.
166
Ehrlich M. DNA methylation in cancer: too much, but also too little. Oncogene 2002; 21(35): 5400-13.
167
Nitert MD, Dayeh T, Volkov P, et al. Impact of an exercise intervention on DNA methylation in skeletal muscle from first-degree relatives of patients with type 2 diabetes. Diabetes 2012; 61(12): 3322-32.
168
Voisin S, Eynon N, Yan X, Bishop DJ. Exercise training and DNA methylation in humans. Acta Physiol (Oxf) 2015; 213(1): 39-59.
169
Rönn T, Volkov P, Davegårdh C, et al. A six months exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue. PLoS Genet 2013; 9(6): e1003572.
170
Eynon N, Morán M, Birk R, Lucia A. The champions’ mitochondria: Is it genetically determined? A review on mitochondrial DNA and elite athletic performance. Physiol Genomics 2011; 43(13): 789-98.
171
Alibegovic AC, Sonne MP, Højbjerre L, et al. Insulin resistance induced by physical inactivity is associated with multiple transcriptional changes in skeletal muscle in young men. Am J Physiol Endocrinol Metab 2010; 299(5): E752-63.
172
van Dijk SJ, Tellam RL, Morrison JL, Muhlhausler BS, Molloy PL. Recent developments on the role of epigenetics in obesity and metabolic disease. Clin Epigenetics 2015; 7(1): 66.
173
Brown WM. Exercise-associated DNA methylation change in skeletal muscle and the importance of imprinted genes: a bioinformatics meta-analysis. Br J Sports Med 2015; 49(24): 1567-78.
174
Champagne FA. Early adversity and developmental outcomes interaction between genetics, epigenetics, and social experiences across the life span. Perspect Psychol Sci 2010; 5(5): 564-74.
175
McGee SL, Fairlie E, Garnham AP, Hargreaves M. Exercise-induced histone modifications in human skeletal muscle. J Physiol 2009; 587(Pt 24): 5951-8.
176
McKinsey TA, Zhang CL, Olson EN. Control of muscle development by dueling HATs and HDACs. Curr Opin Genet Dev 2001; 11(5): 497-504.
177
Potthoff MJ, Wu H, Arnold MA, et al. Histone deacetylase degradation and MEF2 activation promote the formation of slow-twitch myofibers. J Clin Invest 2007; 117(9): 2459-67.
178
Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M. An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489(7414): 57-74.
179
Guay C, Roggli E, Nesca V, Jacovetti C, Regazzi R. Diabetes mellitus, a microRNA-related disease? Transl Res 2011; 157(4): 253-64.
180
Davidsen PK, Gallagher IJ, Hartman JW, et al. High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression. J Appl Physiol 2011; 110(2): 309-17.
181
Bye A, Røsjø H, Aspenes ST, Condorelli G, Omland T, Wisløff U. Circulating microRNAs and aerobic fitness the HUNT-Study. PLoS One 2013; 8(2): e57496.
182
Mooren FC, Viereck J, Krüger K, Thum T. Circulating microRNAs as potential biomarkers of aerobic exercise capacity. Am J Physiol Heart Circ Physiol 2014; 306(4): H557-63.
183
Timmons JA, Knudsen S, Rankinen T, et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 2010; 108(6): 1487-96.
184
Gertz J, Varley KE, Reddy TE, et al. Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet 2011; 7(8): e1002228.
185
Friso S, Choi SW, Girelli D, et al. A common mutation in the 5,10-methylenetetrahydrofolate reductase gene affects genomic DNA methylation through an interaction with folate status. Proc Natl Acad Sci USA 2002; 99(8): 5606-11.
186
Terruzzi I, Senesi P, Montesano A, et al. Genetic polymorphisms of the enzymes involved in DNA methylation and synthesis in elite athletes. Physiol Genomics 2011; 43(16): 965-73.
187
Zarebska A, Ahmetov II, Sawczyn S, et al. Association of the MTHFR 1298A>C (rs1801131) polymorphism with speed and strength sports in Russian and Polish athletes. J Sports Sci 2014; 32(4): 375-82.
188
Brøns C, Jacobsen S, Nilsson E, et al. Deoxyribonucleic acid methylation and gene expression of PPARGC1A in human muscle is influenced by high-fat overfeeding in a birth-weight-dependent manner. J Clin Endocrinol Metab 2010; 95(6): 3048-56.
189
Niculescu MD, Zeisel SH. Diet, methyl donors and DNA methylation: Interactions between dietary folate, methionine and choline. J Nutr 2002; 132(8)(Suppl.): 2333S-5S.
190
Shelnutt KP, Kauwell GP, Gregory JF III, et al. Methylenetetrahydrofolate reductase 677C-->T polymorphism affects DNA methylation in response to controlled folate intake in young women. J Nutr Biochem 2004; 15(9): 554-60.
191
Ntanasis-Stathopoulos J, Tzanninis JG, Philippou A, Koutsilieris M. Epigenetic regulation on gene expression induced by physical exercise. J Musculoskelet Neuronal Interact 2013; 13(2): 133-46.
192
Sanchis-Gomar F, Garcia-Gimenez JL, Perez-Quilis C, Gomez-Cabrera MC, Pallardo FV, Lippi G. Physical exercise as an epigenetic modulator: Eustress, the “positive stress” as an effector of gene expression. J Strength Cond Res 2012; 26(12): 3469-72.
193
Pareja-Galeano H, Sanchis-Gomar F, García-Giménez JL. Physical exercise and epigenetic modulation: Elucidating intricate mechanisms. Sports Med 2014; 44(4): 429-36.
194
Fraga MF, Ballestar E, Paz MF, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci USA 2005; 102(30): 10604-9.
195
Kaprio J, Tuomilehto J, Koskenvuo M, et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 1992; 35(11): 1060-7.
196
Yehuda R, Engel SM, Brand SR, Seckl J, Marcus SM, Berkowitz GS. Transgenerational effects of posttraumatic stress disorder in babies of mothers exposed to the World Trade Center attacks during pregnancy. J Clin Endocrinol Metab 2005; 90(7): 4115-8.
197
Yehuda R, Cai G, Golier JA, et al. Gene expression patterns associated with posttraumatic stress disorder following exposure to the World Trade Center attacks. Biol Psychiatry 2009; 66(7): 708-11.
198
Dias BG, Ressler KJ. Parental olfactory experience influences behavior and neural structure in subsequent generations. Nat Neurosci 2014; 17(1): 89-96.
199
Weaver IC, Champagne FA, Brown SE, et al. Reversal of maternal programming of stress responses in adult offspring through methyl supplementation: Altering epigenetic marking later in life. J Neurosci 2005; 25(47): 11045-54.
200
Alegría-Torres JA, Baccarelli A, Bollati V. Epigenetics and lifestyle. Epigenomics 2011; 3(3): 267-77.
201
Chicharro JL, Hoyos J, Gómez-Gallego F, et al. Mutations in the hereditary haemochromatosis gene HFE in professional endurance athletes. Br J Sports Med 2004; 38(4): 418-21.
202
Burt MJ, George PM, Upton JD, et al. The significance of haemochromatosis gene mutations in the general population: Implications for screening. Gut 1998; 43(6): 830-6.
203
Beard J, Tobin B. Iron status and exercise. Am J Clin Nutr 2000; 72(2)(Suppl.): 594S-7S.
204
Deugnier Y, Loréal O, Carré F, et al. Increased body iron stores in elite road cyclists. Med Sci Sports Exerc 2002; 34(5): 876-80.
205
Stein DJ, Newman TK, Savitz J, Ramesar R. Warriors versus worriers: The role of COMT gene variants. CNS Spectr 2006; 11(10): 745-8.
206
Nemeroff CB. Neurobiological consequences of childhood trauma. J Clin Psychiatry 2004; 65(S1)(Suppl. 1): 18-28.
207
Robertson EY, Saunders PU, Pyne DB, Aughey RJ, Anson JM, Gore CJ. Reproducibility of performance changes to simulated live high/train low altitude. Med Sci Sports Exerc 2010; 42(2): 394-401.
208
Hayden EC. Technology: The $1,000 genome. Nature 2014; 507(7492): 294-5.
209
Jones N, Kiely J, Suraci B, et al. A genetic-based algorithm for personalized resistance training. Biol Sport 2016; 33(2): 117-26.
210
Monnerat-Cahli G, Paulúcio D, Moura Neto RS. Letter to the editor: Are the doors opened to a genetic-based algorithm for personalized resistance training. Biol Sport 2017; 34(1): 27-9.
211
Kikuchi N, Nakazato K. Effective utilization of genetic information for athletes and coaches: Focus on ACTN3 R577X polymorphism. J Exerc Nutrition Biochem 2015; 19(3): 157-64.
212
Camera DM, Smiles WJ, Hawley JA. Exercise-induced skeletal muscle signaling pathways and human athletic performance. Free Radic Biol Med 2016; 98: 131-43.
213
Thanassoulis G, Peloso GM, Pencina MJ, et al. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: The Framingham Heart Study. Circ Cardiovasc Genet 2012; 5(1): 113-21.
214
Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008; 359(21): 2208-19.
215
Ruiz JR, Arteta D, Buxens A, et al. Can we identify a power-oriented polygenic profile? J Appl Physiol 2010; 108(3): 561-6.
216
Webborn N, Williams A, McNamee M, et al. Direct-to-consumer genetic testing for predicting sports performance and talent identification: Consensus statement. Br J Sports Med 2015; 49(23): 1486-91.
217
Bouchard C, Sarzynski MA, Rice TK, et al. Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. J Appl Physiol 2011; 110(5): 1160-70.
218
Meckel Y, Ben-Zaken S, Nemet D, Dror N, Eliakim A. Practical uses of genetic profile assessment in athletic training–an illustrative case study. Acta Kinesiol 2014; 20: 25-39.
219
Williams A, Wackerhage H, Day S. Genetic Testing for Sports Performance, Responses to Training and Injury Risk: Practical and Ethical Considerations. Genetics and Sports 2nd ed. 2016; 105-19.
220
Vlahovich N, Fricker PA, Brown MA, Hughes D. Ethics of genetic testing and research in sport: A position statement from the Australian Institute of Sport. Br J Sports Med 2017; 51(1): 5-11.
221
Grimaldi KA, Paoli A, Smith GJ. Personal genetics: Sports utility vehicle? Recent Pat DNA Gene Seq 2012; 6(3): 209-15.
222
Heffernan SM, Kilduff LP, Day SH, Pitsiladis YP, Williams AG. Genomics in rugby union: A review and future prospects. Eur J Sport Sci 2015; 15(6): 460-8.
223
Dennis C. Rugby team converts to give gene tests a try. Nature 2005; 434(7031): 260-0.