Training in Line W/ Your Genetic Potential Can Boost Your Performance Gains More Than 600%, DNAFit™ Studies Say

Training in Line W/ Your Genetic Potential Can Boost Your Performance Gains More Than 600%, DNAFit™ Studies Say

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While the study at hand appears to confirm that the DNAFit test can tell you if you're an endurance or strength athlete, it won't help you achieve goals you were not "made for" - it eventually you may thus have to give up your dream of being the fastest, strongest or most chiseled guy / gal on the track, field or in gym.

You probably know that: There's that guy at the gym who has been training only half as long as you and still made twice the gains, ... must be juicing that idiot, right? Well, even if we assume that you're not one of the >50% of trainees who overtrain (and undereat) that's by no means the most likely explanation for the astonishing discrepancies.

A recent study that was conducted by a consortium of European researchers is now the first to impressively demonstrate that "matching the individual’s genotype with the appropriate training modality leads to more effective resistance training" (Jones. 2016) What the scientists some of whom work for a company that offers corresponding DNA tests won't tell you, though is that their test will eventually just help you to select the right sport, not to excel in the one sport you have already chosen.

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Eventually, none of this should surprise you, though. Scientists and practitioners alike have suspected for centuries and known for decades that elite athletes are born, not formed in the gym. Association studies have identified dozens of genetic variants linked to training responses and sport-related traits (Table 1 provides a glimpse at the peak of a hitherto largely unknown iceberg of genetic variants that will influence your adaptation to specific training types).

Table 1: List of known genetic variants that influence your adaptation to specific (resistance) training stimuli that were analyzed with the patented DNAFit Peak Performance Algorithm™ in the study at hand (Jones. 2016).

Yes, the way and consistence with which you train will obviously have an effect on the way your physique, strength, speed, conditioning, etc. develops, but when all is said and done, you are simply lucky if you're not lapped by somebody who has trained just as intense- and persistenly who was gifted with a more appropriate gene set for the sports you love. It is thus no wonder that scientists have been pondering about ways to (a) select the right candidates for the right sports and (b) personalizing athletes' training based on their genetic profiles.

In the previously cited study, Jones et al. proposed to do just that by the means of an algorithm that would allow athletes to achieve "greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities" (Jones. 2016).The DNAFit algorithm which is designed to predict the response to high- or low-intensity resistance training programs invokes the 15 performance-associated gene polymorphisms from Table 1.

Figure 1: Both studies used the same randomized, double-blinded crossover design (based on Jones. 2016).

To validate it, its designers from DNA Sports Performance Ltd. and scientists from the University of Central Lancashire, the Universitat Pompeu Fabra and the Parc Científic i Tecnològic Agroalimentari de LleidaPCiTAL performed two studies in independent cohorts of male athletes using in ...

• study 1: athletes from different sports (n=28) / 55 Caucasian male University athletes, all aged 18-20 years, volunteered for the study, and 28 of them (height 180.7 ± 1.5 cm, weight 77.0 ± 2.1 kg) successfully completed it (27 athletes had not completed all aspects of the study due to either injury or illness); each participant was a member of first or second team, actively competing in British Universities and Colleges Sports (BUCS) leagues. The athletes competed in squash (n = 1), swimming (n = 7), running (n = 1), ski/snowboard (n = 4), soccer (n = 1), lacrosse (n = 2), badminton (n = 1), motorsport (n = 1), cycling (n = 4), cricket (n = 2), volleyball (n = 1), fencing (n = 1) and rugby union (n = 2). and 
• study 2: soccer players (n=39) / 68 male soccer players, all aged 16-19 years, volunteered to participate in the study, and 39 of them (height 176.1 ± 1.0 cm, weight 68.9 ± 1.5 kg) successfully completed it (29 participants were withdrawn from the study due to non-adherence of set training volumes over the 8 weeks, or injury); each subject was a member of college soccer academy who actively competed in British Universities & Colleges Sport (BUCS) league.

In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. In that, participants of both studies were initially randomly allocated to an eight-week high- or low-intensity resistance-training program, after undergoing performance tests for both explosive power and endurance. After another set of performance tests, they then transitioned to the respective other 8-week intervention, the results of which were then compared with the previous ones and correlated with the subjects gene types.

No, the muscle or strength gains were not assessed: I am not sure why the scientists decided against measuring the lean / fat mass gains / losses. After all, their gene set included the thyrotropin-releasing hormone (TRH) receptor gene where polymorphisms at rs16892496 A/C that influences the secretion of thyroid-stimulating hormone (TSH) and prolactin (PRL) and has been found to modulate the amount of lean mass by Liu et al. in 2009. My best bets are that the reasons are financial ones (DXA is expensive, everything else inaccurate)strategic ones, with 8-weeks of training being unlikely to produce sufficiently inter-group differences in already trained athletes, given the small sample size(s) and range of sports that were included (esp. in study 1), or a mere consequence of the choice of protocols, which did not include a hypertrophy protocol (thus no measurement of muscle gains) and/or would obviously produce greater strength gains with the high intensity protocol (measuring those would thus be useless, too).

As the authors point out, "[t]he study was double blinded, in that all were unaware of their ‘genetic potential status’, as determined by the DNAFit Peak Performance Algorithm™" (Jones. 2016). Since this also included the lead investigator who coached the participants during the 8 weeks of resistance training, the notion that 'this is the optimal training type for me / my trainee' should not have influenced the study outcomes.

Figure 2: Intergroup comparisons of CMJ increases (%) in response to high- or low-intensity training; the %-ages over the bars indicate the difference to the mean effect (all) - It's easy to see that training 'according to your genotype' makes a 40-80% difference even if you compare the speficic to the average success; >100% for inter-group comparisons (Jones. 2016)

And still, as the data from the explosive power and aerobic fitness tests that involved countermovement jumps (CMJ) and an aerobic 3-min cycle test (Aero3) revelead, training 'according' to your genes (or rather the assessment of the DNAFit test), i.e.

• high-intensity trained with power genotype or 
• low-intensity trained with endurance genotype,

significantly increased results in CMJ (P=0.0005) and Aero3 (P=0.0004). Athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low intensity trained with power genotype), however, demonstrated non-significant improvements in CMJ (P=0.175) and less prominent results in Aero3 (P=0.0134).

Figure 3: Inter-group comparisons of Aero3 increases (%) in response to high- or low-intensity training; left axes = power and endurance genes groups, right axes = all subjects (data from both cohorts | Jones. 2016).

Similar results were observed in the 2nd study, where  soccer players from the matched groups saw significantly greater (P<0.0001) performance changes in both tests compared to the mismatched group. In that, the following facts are particularly noteworthy:

• the advantage of training 'according to your genotype' ranges from ~40% to ~80% even if you compare it to the average training response (Figure 1, "all");
• comparing training according to training in discordance with your genotype(s) yields differences that range from 55% up to 610% (the latter in the soccer players on the low intensity regimen for CMJ; Figure 1, study 2 / low intensity)

What is maybe even more important than the statistically significant differences in the mean gains is the consistency of failure, i.e. the fact that Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P<0.0001).

Remember? Your Post-Workout Testosterone Levels Can Predict Your Gains - Study Takes Novel Approach to the T ↔ Muscle Link | Learn more

Bottom line: As the (maybe biased) authors of the study point out, their well-designed and appropriately blinded trial clearly "indicate[s] that matching the individual’s genotype with the appropriate training modality [as determined with 'their' proprietary DNAFit test] leads to more effective resistance training" (Jones. 2016). It does therefore stand to reason that "[t]he developed algorithm may be used to guide individualised resistance-training interventions" (Jones. 2016). Whether that's actually useful for the average gymrat, whose goal may diverge sign. from what he was 'born to achieve', though, is another story... at least until you'll be able to home-brew / -tweak your genes with CRISPER ;-)

Another thing we shouldn't forget is that getting big and buffed, the goal of a majority of male gymgoers, wasn't even investigated in the study at hand... I bet, though, that future studies with different training regimen and study populations (e.g. untrained individuals) will assess and probably find similar results for muscle and strength gains - And you know where you will be able to read about their results, right? | Comment on Facebook!

References:

• Jones, N., et al. "A genetic-based algorithm for personalized resistance training." Biol Sport 33.2 (2016): 117-126.
• Liu, Xiao-Gang, et al. "Genome-wide association and replication studies identified TRHR as an important gene for lean body mass." The American Journal of Human Genetics 84.3 (2009): 418-423.

Calculated Energy Requirements ±15% & More Off of True Resting Energy Expenditure | Genotyping May Help Maintain Muscle While Dieting -- ISSN'15 Research Review Issue #5

Calculated Energy Requirements ±15% & More Off of True Resting Energy Expenditure | Genotyping May Help Maintain Muscle While Dieting -- ISSN'15 Research Review Issue #5

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In view of the inaccuracy of the standard equations that are used to calculate our energy requirements we are approaching the age of the "misquantified self".

If I had to find a common theme in the studies discussed in this installment of the ISSN'15 Research Overview, I guess it would be "get lean and stay lean". If you asked for a common bottom line, it would be: It's complicated.

Complicated, because even though our genes appear to have a determining rule in what's the best diet for us, the "calories in vs. calories out" equation is eventually going to determine whether you are losing or gaining weight. That's problematic, not just because many of us have lost their inborn ability to match their energy intake to their individual requirements. It's also problematic in view of the increasing number of people who rely on the numbers of apps and fitness-trackers which are just as unreliable as the standard equations that were fed into the source code to calculate them.

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• Eating according to your genes may help you retain lean mass -- You will have noticed that the number of companies that do gene testing in order to tell you "exactly" what and how to eat is exploding lately. Unfortunately, the same cannot be said of research that would confirm that any of the test results people often pay several hundred dollars for are worth the paper they are printed on.
  
  Against that background, a recent study from the Texas A&M University comes quite handy for the shareholder value of said companies. The corresponding poster presentation at the ISSN Conference 2015 expanded on the results of a 2015 study by Coletta et al. (2015a), in which the researchers observed "that correctly matching diet type to some obesity-related genes promoted greater fat loss during the first 3 months of a diet and exercise intervention" (Coletta. 2015b). In the study at hand the researchers did now examine "whether these changes were observed following a 6-month diet and exercise training program" (ibid.).
  
  Over the course of the 6-months study, fifty sedentary, obese women (41.6 ± 12 yrs, 35.4 ± 8 kg/m²) who had been genotyped before the intervention, were either truly matched (T) to their diet group based on genotype (n = 28) or falsely matched (F) based on genotype (n = 22).
  

  

  Figure 1: Macronutrient composition of the diets (Coletta. 2015b).

  Irrespective of the group they were assigned to, all subjects consumed the same amount of energy (1,500 kcal/d), but with either a high or low ratio of carbohydrate:fat:protein percentages (see Figure 1). But the women didn't just eat less, they also had to perform a supervised circuit-style resistance-exercise program four days/week and followed a standardized walking program that consisted of 10,000 steps/day on three days/week. 

High carbohydrate diets for metabolic syndrome? A question of your genes - Study suggests that diets high in carbohydrate may not be appropriate for rs328 G carriers with the metabolic syndrome. In said study, two districts in Shanghai, China were randomly selected to be the intervention and control group, and patients (n=235) with metabolic syndrome within these two districts were selected based on a multistage sampling method.

"Three Days on Pasta, Muffin & Bread Diet (84% CHO) = 1kg Add. Lean Mass and a Sign. Trend for Decreased Fat Mass" - Probably the subjects in the study discussed in this SV Classic Article simply had the right genes ;-)

Fasting glucose was reduced in rs328 CC homozygotes (p=0.028) but not G carriers (p=0.686) within the intervention group.

Also an ancillary study with greater statistical power by combining the baseline measurements across both the intervention and control groups was conducted to test the cross-sectional statistical interactions between carbohydrate/fat and lipoprotein lipase genotypes for homeostasis model assessment of insulin resistance/insulin/fasting glucose. Increased carbohydrate intakes were positively associated with homeostasis model assessment of insulin resistance and insulin in rs328 G carriers but not CC homozygotes (p for interaction was 0.025).

"These results indicate that diet high in carbohydrate may not be suitable for metabolic syndrome rs328 G carriers, calling for the development of personalized dietary intervention for metabolic syndrome subjects," (Zhang. 2015) scientists say.

• Unsurprisingly, the combination of both, diet and exercise triggered significant reductions in weight and body fat in both diet groups (high and low carb). Nevertheless, both, the carbohydrate content of the diet and the genotype < > diet match, had significant effects on the study outcomes, as well. More specifically, ...
  • the participants following a more carbohydrate restricted diet experienced significantly greater weight loss and slightly greater body composition changes (the low-carb-fat-loss-advantage), and
  • matching diet based on gene-type exhibited better retention of fat free mass, albeit with no significant differences between groups in changes in weight or fat mass (the genotype-lean-mass-advantage).
  Now that sounds pretty much like genotyping your diet was always the way to go, right? Well, in view of the fact that the falsely assigned subjects experienced a slightly greater reduction in body fat percentage, the interpretation of the study results does actually depend on one's individual goals and is thus less obvious than it may appear to be when you read the abstract.
• How much should you eat, ladies? Study shows: No equation can answer this question exactly -- You may believe that your apps and fitness trackers were able to tell you "exactly" how much energy you need, but  eventually their recommendations are also based on equations like those Kisiolek et al. tested in their latest study.
  
  

  

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  For the experiment on which I base the above statement, the scientists recruited twenty-five recreationally active, college-aged women (20.72 ± 0.97 yrs; 163.04 ± 5.67 cm; 67.08 ± 10.40 kg; 29.04 ± 5.80% BF) who underwent a single day of testing, consisting of determination of REE by indirect calorimetry (TrueOne® 2400 Metabolic Measurement system, ParvoMedics, Sandy, UT) followed by body composition assessment.
  
  To avoid interferences by exercise or dietary factors, all subjects were instructed to refrain from strenuous exercise 48 hrs prior to testing in addition to fasting >8 hrs prior.
  
  During the actual testing, the participants laid motionless without falling asleep for 15-20 minutes during REE determination. Data were recorded during a period of time in which criterion variables (e.g., VO2 L/min) changed less than 5% every 5 minutes. In addition, the subjects' body composition was assessed using air displacement plethysmography (BODPOD, Cosmed, USA) via the Siri equatio and fed into the three equations the scientists tested, i.e. the (1) Nelson Equation the (2) Mifflin-St. Jeor Equation and the (3) Harris-Benedict Equation (with a moderate activity factor).
  

  

  Figure 2: Energy expenditure (kcal/24h) according to indirect calorimetry (measured) and the three tested equations (calculated); %-ages indicate differences to measured values (Kisiolek. 2015).

  For all three tested equations the results were significantly different than indirect calorimetry (p < 0.001; see Table 1). More specifically, ...
  • the Nelson and Mifflin-St. Jeor equations underestimated REE when compared to indirect calorimetry by 345.5 ± 51.5 and 220.6 ± 47.3 kcals, respectively,
  • while the Harris Benedict overestimated REE by 272.4 ± 49.3 kcals.
  Against that background it could be considered a "success" that all three equations were moderately correlated with the subjects' objectively measured resting energy expenditure (REE) as determined by indirect calorimetry.
  
  Well, "success" or not, the implications of Kisiolek's study should be clear: "Practitioners should exercise caution when providing dietary recommendations based upon predicted REE values as certain equations may over or underestimate energy requirements by several hundred kilocalories" (Kisiolek. 2015); and I would like to add: If you want to make sure you're nailing it, log your dietary intake during a weight stable week - that's the only way to know for sure how much energy you need.

So what didn't make the "cut"? Worth mentioning, but not discussing in detail are the observations Mullins et al. made when they investigated the effects of Iron Cuts®, a thermogenic supplement from MusclePharm, that appeared to improve the subjects strength, but had no significant effects on the health or body composition of 20 recreationally trained men who participated in Mullins' prospective, double-blind, placebo controlled randomized trial (Mullins. 2015).

Statistical significance was only observed for the small increase in fatty acid oxidation in response to Shred-Matrix® from -3h to pre-workout not for the post increase (Seijo. 2015). Neither of them means that the supplement will actively promote fat loss, though.

The increase in fatty acid oxidation Seijo et al. observed when they studies the acute effects of Shred-Matrix® on fat oxidation is certainly more impressive than the results of Mullins study, but eventually it is of even less practical relevance. As a SuppVersity reader you should now that the currently available research refutes the existence of a reliable mechanistic link between the acute increases in fatty acid oxidation the scientists observed before and after the workout and long-term fat loss (the post-workout increase in fatty acid oxidation was not even significantly higher than the increase in the placebo group, by the way).

In view of the lack of effect on mood state and perception of hunger it is thus very questionable, whether the supplement can actually do what the scientists say their results would "suggest" and augment "the weight-loss benefits at rest and during exercise" (Seijo. 2015) | Comment!

References:

• Coletta, A., et al. "Influence of Obesity-Related Genotype on Weight Loss Success and Body Composition Changes While Participating in an a 3-Month Exercise and Weight Loss Program: Preliminary Findings." The FASEB Journal 29.1 Supplement (2015a): LB241.
• Coletta, A., et al. "Effects of matching diet type to obesity-related genotype on body composition changes in women during a six-month resistance-exercise training and walking program." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015b): P16.
• Kisiolek, J., et al. "A comparison of resting energy prediction equations in young recreationally active women." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P50.
• Mullins, Jacy, et al. "Safety and efficacy of a proprietary thermogenic and cutting agent on measures of muscular strength and endurance, body composition, fat metabolism, and hormone levels." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P13.
• Seijo, Marcos, et al. "Effectiveness of multi-ingredient supplement on substrate utilisation, perception of hunger, mood state and rate of perceived exertion (RPE) at rest and during exercise." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P42.
• Zhang, Shixiu, et al. "Diets high in carbohydrate may not be appropriate for rs328 G carriers with the metabolic syndrome." Asia Pac J Clin Nutr 24.3 (2015): Ahead of print