We had discussed earlier that your response to training is directly linked to your genetics and lineage, then your age, gender, caste, race, the initial level of fitness or experience. This has been confirmed as true by many studies, not only for strength but also for many other fitness parameters.
Also, we discussed the concept of hard gainers and easy gainers as applied to the general distribution of a population. We concluded that lifters should be subjected to individualized training rather than off the shelf programs in order to get maximum effects.
But now you may wonder, how to know what type of genetic potential do you possess? We’ll try to explain it in the next section.
Know Your Potential
Some coaches like to classify trainees according to the dominance of their muscle fiber make-up. Those with more fast twitch fibers are ideal for power sports like sprinting, throwing, weightlifting etc, and those with slow twitch fibers tend to perform better in endurance sports like long distance running, marathon racing etc. The general method employed to determine it is by performing max reps with 80% of 1 Rep max for different upper and lower body exercises. Less than 8 reps make you fast twitch dominant, and more than 8 makes you slow twitch.
Another method for determining muscle building potential is based on Dr. Casey Butt’s formula. It is based on the research of joint measurements of many old timer natural (allegedly) bodybuilders. This formula implies that your muscle size is a direct function of your joint size. Here’s the formula:
The variables in the formula are as follows:
Max. LBM is calculated in pounds.
H = Height in inches
A = Ankle circumference at the smallest point
W = Wrist circumference measured on the hand side of the styloid process.
(The styloid process is the bony lump on the outside of your wrist.)
%bf = The body fat percentage at which you want to predict your maximum lean body mass
The methods above have some limitations to them, and can’t be deemed as accurate figuring of one’s genetic potential. In the former method, it is not practical to systematically utilize one rep max testing for certain exercises which isolate particular muscles to determine the fast to slow twitch ratio in that exact muscle group. Additionally, other factors like faulty exercise technique and nervous system inefficiency also impact the results of this test. Moreover, training specificity in certain sports can also muck up the results, as it’s been shown that with long enough training type II A muscle fibers can acquire the characteristics of type II B fibers.
In the latter method, the formula used has shown sufficient degree of accuracy when it comes to most of the normal population. But still, there exists a subsection of a few strength athletes who continue to defy this formula. For example, an average bodybuilder/athlete of 5’7” height with a 7” wrist and 9” ankle with fully visible abs at a ripped 9% body fat is calculated to possess a maximum lean body mass of 75 Kgs and no more.
This calculation fits most of the regular gym goers. Yet we witness, on certain occasions, strength athletes of normal stature weighing 80 to 90 kgs with sometimes even lower body fat. Even considering a margin of 5% error in this formula still, it doesn’t completely explain a modern natural strength athletes body composition improvements. Certainly, genetic variation along with advanced supplementation, nutritional protocols and training have something to do with it.
There could be one more fact responsible for the inaccuracy of this formula. The elite bodybuilders in the vintage era before the advent of steroids were not really as elite as compared to the competitors today. Because at that time bodybuilding was considered an obscure sport and not much money was involved in it. Most of the athletes were not interested in participating in it. So chances are that much of the potential was left to be evaluated in Dr. Butt’s research.
Genetic Variation In Strength, Mass & Performance
There is a great deal of variation in genetic factors which Butt’s formula doesn’t take into account. These factors are very important from the physiological and biochemical point of view and are responsible for the muscle building process in the body.
Studies (2,5,7) have shown that those who are super responders to training have better satellite cell activation which leads to greater gains in muscle cross-section area. The better up-regulation of other growth factors like MGF, IGF-IEa, and Myogenin has also been observed in super responders (3,4,8).
It has also been shown that your genes influence your ‘fatness’ levels through factors like nutrient partitioning, energy intake, and expenditure. The metabolically impaired individuals gain much more weight in response to the same caloric surplus as compared to the metabolically blessed. These studies suggest that some people are actually more prone to get fat than others (6, 10-21).
The Good News
Modern science has a pretty deep understanding of the human genome, and genetic testing & muscle biopsy can reveal all the good information you ever need to know about your training. You’ll be relieved to know that you don’t have to undergo a surgical operation to determine your fiber type. There is sufficient research on muscle fiber type and composition to pretty accurately determine appropriate training applications. Also, the availability of EMG data helps in selecting correct parameters of training to your advantage.
It is quite possible that in future individualized training and nutrition plans based on a person’s genetic profile may be prescribed. But we’re still not there, and till then we need to create well-informed plans based on scientific knowledge, experience, and wisdom.
In the final installment of this series, we’ll learn how to put together a scientific plan to reach your true potential.
- Nazarov IB, Woods DR, Montgomery HE, Shneider OV, Kazakov VI, Tomilin NV, Rogozkin VA (2001) The angiotensin converting enzyme I/D polymorphism in Russian athletes. Eur J Hum Genet 9:797–801, 2001.
- Petrella JK, Kim JS, Mayhew DL, Cross JM, Bamman MM. Potent myofiber hypertrophy during resistance training in humans is associated with satellite cell-mediated myonuclear addition: a cluster analysis. J Appl Physiol 104: 1736–1742, 2008.
- Yang N, MacArthur DG, Gulbin JP, Hahn AG, Beggs AH, Easteal S, North K. ACTN3 genotype is associated with human elite athletic performance.Am J Hum Genet 73: 627–631, 2003.
- Bamman MM, Petrella JK, Kim JS, Mayhew DL, Cross JM. Cluster analysis tests the importance of myogenic gene expression during myofiber hypertrophy in humans. J Appl Physiol 102: 2232–2239, 2007.
- Riechman SE, Balasekaran G, Roth SM, Ferrell RE. Association of interleukin-15 protein and interleukin-15 receptor genetic variation with resistance exercise training responses. J Appl Physiol 97: 2214–2219, 2004.
- O’Rahilly S., Farooqi I.S. Genetics of obesity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361:1095–1105, 2006.
- Timmons JA. Variability in training-induced skeletal muscle adaptation. J Appl Physiol [Epub ahead of print], 2010.
- Cauci S, Santolo M, Ryckmann KK, Williams SM, Banfi F. Variable number of tandem repeat polymorphisms of the interleukin-1 receptor antagonist gene IL-1RN: a novel association with the athlete status. BMC Med Genet 11(29) 2010.
- Dennis RA, Zhu H, Kortebein PM, Bush HM, Harvey JF, Sullivan DH, Peterson CA. Muscle expression of genes associated with inflammation, growth, and remodeling is strongly correlated in older adults with resistance training outcomes. Physiol Genomics 38(2):169-75, 2009.
- Bouchard C, Tremblay A, Despres JP, Nadeau A, Lupien PJ, Theriault G, Dussault J, Moorjani S, Pinault S, Fournier G. The response to long-term overfeeding in identical twins. N Engl J Med. 322(21):1477–1482, 1990.
- Bouchard C, Tremblay A, Despres JP, Theriault G, Nadeau A, Lupien PJ, Moorjani S, Prudhomme D, Fournier G. The response to exercise with constant energy intake in identical twins. Obes Res 2:400–410, 1994.
- Perusse L, Despres JP, Lemieux S, Rice T, Rao DC, Bouchard C. Familial aggregation of abdominal visceral fat level: results from the Quebec family study. Metabolism 45:378–382, 1996.
- Bouchard C, Tremblay A. Genetic effects in human energy expenditure components. Int. J. Obes 49–55. discussion 55–8, 1990.
- Loos RJ and Bouchard C. Obesity – is it a genetic disorder? J Intern Med 254(5) 401-25, 2003.
- Cotsapas C, Speliotes EK, Hatoum IJ, et al.: Common body mass index-associated variants confer risk of extreme obesity. Hum Mol Genet 18:3502–3507, 2009.
- Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, Perusse L, Bouchard C. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 14(4):529–644, 2006.
- Fawcett KA, Barroso I. The genetics of obesity: FTO leads the way. Trends Genet. pp. 266–274, 2010.
- Tercjak M, Luczynski W, Wawrusiewicz-Kurylonek N, Bossowski A. The role of FTO gene polymorphism in the pathogenesis of obesity. Pediatr Endocrinol Diabetes Metab 16(2) 109-13, 2010.
- Herrera B and Lindgren C. The genetics of obesity. Curr Diab Rep 10:498-505, 2010.
- Faith MS, Rha SS, Neale MC, Allison DB. Evidence for genetic influences on human energy intake: results from a twin study using measured observations. Behav Genet 29:145–54, 1999.
- Choquette AC, Lemieux S, Tremblay A, Chagnon YC, Bouchard C, Vohl MC, Perusse L. Evidence of a quantitative trait locus for energy and macronutrient intakes on chromosome 3q27.3: the Quebec Family Study. Am J Clin Nutr 88(4): 1142-8, 2008.
- Bray MS, Hagberg JM, Perusse L, Rankinen T, Roth SM, Wolfarth B, Bouchard C. The human gene map for performance and health-related fitness phenotypes: the 2006–2007 update. Med Sci Sports Exerc 41: 35– 73, 2009.