By Nicole Golden
Genetic factors play a role in response to exercise and body composition, as in most physiologic functions in the body. High levels of physical activity have been known to provide benefits beyond just weight loss, such as decreased disease risk, improved strength and mobility, and improvement in life expectancy.
Additionally, physical activity can prevent age-related weight gain and regain of weight lost from a weight loss program (Drenowatz et al., 2015). Physical activity level is a quick and reliable measure of an individual’s physical activity throughout the day. The PAL is defined as the ratio of total daily energy expenditure (TDEE) to basal metabolic rate (BMR).
A PAL of 1.2 would be representative of a relatively sedentary individual, perhaps an office worker, while a PAL of 2.0 would be a highly physically active person who may have an occupation that requires heavy physical labor such as a farmer or competitive swimmer (Summerfield, 2016). Saris et al. (2003) reported the consensus, based on literature review, is that 45 to 60 minutes of moderate to vigorous activity per day correlating to a physical activity level (PAL) of 1.7 is necessary to prevent the transition from overweight to obesity.
Similarly, as much 250 minutes per week of moderate to vigorous physical activity are necessary to prevent weight regain in formerly obese individuals (Saris et al., 2003).
Despite these findings, genetics’ role in a client’s response to exercise remains somewhat unclear in the scientific literature. The heritability of BMI (how much of a person’s BMI is linked to their genes) is estimated to be as much as 40 to 70 percent in a person as the endocrine and metabolic factors which will control an individual’s genetic makeup influences energy expenditure, appetite regulation, thermogenesis, and metabolic functions.
Recent research by Leońska-Duniec et al. 2016 described several single nucleotide polymorphisms (SNP) that affect these processes and can, in turn, make a person more susceptible to obesity through a variety of mechanisms. The correlation between these genetic variants and the response to a resistance training intervention on body composition changes was investigated.
The study design was interesting as the authors utilized a cohort and data from the Bone Estrogen Strength Training (BEST) study, which examined the relationship between strength training and body composition changes in post-menopausal women. The researchers recruited participants from this same study via mailings requesting the original set of participants (n=320) consent to genetic testing to identify if they carried any of the 32 identified SNPs.
Approximately half of the participants from the prior study were re-recruited and underwent DNA testing. The researchers utilized the exercise interventions and body composition data from the BEST study with the caveat of grouping the participants by a calculated genetic predisposition towards obesity.
Participants were recruited from the original BEST study. The women were required to have a BMI of 19 to 33, identify as non-smokers, and identify as post-menopausal though the age range was 30-65 years. Similarly, the participants’ baseline activity level should not have exceeded 120 minutes of low-intensity exercise weekly. The study participants were asked to refrain from making any dietary changes as they should maintain their weight for the duration of the 12 month study period.
The study participants underwent testing at regular intervals. Their strength was assessed every 6 to 8 weeks via an isokinetic dynamometer to provide appropriate progressions and maintain adequate load and training volume. Dual Energy X-ray Absorptiometry (DEXA) scans were completed at baseline and 12 months to provide accurate data on body composition.
Dietary records were assessed at baseline, six months, and 12 months to make sure the participants were not making any dietary changes, which would lead to changes in body composition. Likewise, some of the study participants were not separated based on hormone replacement therapy status. The authors did indicate that more than half were taking some form of HRT regimen.
Buccal swabs for DNA were taken at baseline and tested for 32 SNPs. A genetic risk score (GRS) was calculated based on the number of polymorphisms present in the DNA sample. Specific SNPs were weighted based on their physiologic effect. The participants were then sorted into groups based on low GRS, intermediate GRS, and high GRS. The authors did not indicate what specific criteria were used for the GRS score though they did provide a weight for each in terms of physiologic significance.
The participants were split into two intervention groups in the original BEST study. One group was the intervention/exercise (EX) group, and the other the non-exercising control group (NEX). Participants were asked to engage in supervised moderate to high-intensity resistance training for 75 minutes three times per week for the 12-month intervention period.
The resistance training routines consisted of 2 sets of 6 to 8 repetitions at 70 to 80 percent of participants’ one repetition maximum. The DXA scan was repeated at the end of the study period to assess changes in body composition.
The results indicated the EX group experienced a greater increase in lean soft tissue and a more significant decrease in both body fat percentage and absolute fat mass than the NEX group. Similarly, strength testing demonstrated strength increases were significantly greater in the EX group versus the NEX group. These results are consistent with results of prior studies.
However, there was a significant interaction between the GRS score and the change in weight, total body fat mass, and abdominal fat in the EX group. Overall, the women with a high GRS score tended to gain weight during the study period with a small amount of lean body mass increase as a percentage of total body weight and relatively little fat loss.
Conversely, women in the low GRS category lost the most absolute weight, most significantly decreased fat mass, and increased their percentage of lean body mass. The women categorized as an intermediate GRS still lost weight and improved their body composition, but not as significantly as the low-risk GRS group.
• The EX group experienced significantly more LBM gains and fat loss on average than the NEX group overall despite any genetic risk for obesity as measured by percentage of bodyweight (p<0.05). Strength gains were also significantly higher in the EX group than the NEX group on average as measured by comparing leg strength in each group (p<0.0001).
• The group with a low GRS score lost the most absolute weight and significantly decreased fat mass and increased LBM compared to the other groups.
• The group with an intermediate GRS score decreased fat mass and increased LBM, but, on average, not as much as the group with the low GRS score.
• The group with the high GRS score tended to gain weight during the study period and gained a smaller amount of LBM than the other groups. They experienced little fat loss on average.
Theoretically, knowing the genetic risk of obesity in a client would be beneficial when designing exercise programs and nutrition plans to prevent or treat obesity. However, the real impact of genetic predisposition is relatively low.
Speliotes et al. (2010) examined 2.8 million SNPs in up to 123,865 individuals looking specifically at 42 of these SNPs closely and discovered those in the lowest genetic risk category only had a BMI 2.73 kg/m2 lower than their counterparts in the highest risk group. To elaborate, though genetics does play some role in an individual’s response to an exercise and weight-loss program, it is not a barrier that cannot be overcome.
There are cases in which early intervention with regards to exercise and nutrition planning may be useful in preventing an individual who is overweight from becoming obese, especially if there is a strong family history of obesity. Increased physical activity may be especially useful in this cohort, as some studies have demonstrated physical activity significantly off-sets any genetic risk for obesity. Overall, there is not enough data to suggest it is efficacious to design treatment plans for obesity based solely on genetic risk (Ng & Bowden, 2013).
Overall, the study is very well designed, making it difficult to highlight significant limitations. The authors controlled for potential confounders in the participant selection criteria such as prior exercise experience, smoking, significantly high or low BMI at baseline, etc. Likewise, the statistical analysis was appropriate for this data set. However, there are a few potential limitations to this experiment.
First, the researchers calculated a GRS for the women but did not differentiate between which SNPs the study participants had. In other words, the GRS lumped participants with different genetic markers into the same risk category. Similarly, the authors based this study on the results of previous meta-analyses of the genetic risk of obesity and specific SNPs.
Also, the researchers only examined the effect of the genes in a group of post-menopausal women. These findings may not apply to other segments of the population (e.g., pre-menopausal women, men, or children).
CONNECTION TO PRACTICE AS AN NASM CERTIFIED PERSONAL TRAINER
This study provides useful information on the effects of a strength training regimen on body composition in a group of post-menopausal women. Likewise, the study also reinforced the benefits of resistance training in altering body composition and improving strength. However, based on the current evidence, though statistically significant body composition improvement variations are too small to justify the expense in identifying those at genetic risk for obesity before prescribing an exercise program to these individuals.
It may be beneficial as an NASM CPT or Certified Nutrition Coach (CNC) to take a thorough family history upon assessing a client to determine if obesity is something that runs in their family. Similarly, the CNC can conduct a complete history of the client’s prior weight loss attempts and their lifetime history of obesity to subjectively determine if there is the potential for this genetic risk.
This may allow the CNC to help the client set appropriate SMARTS goals involving improved dietary and exercise practices to help them move towards a long-term weight loss goal. This knowledge may help the client understand that although progress may be slow goals will still be attainable.
It is important to note that improving nutrition practices and incorporating moderate to vigorous exercise (including resistance training) remains the standard recommendation for anyone desiring to prevent or treat obesity regardless of genetic risk.
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Klimentidis, Y. C., Bea, J. W., Lohman, T., Hsieh, P.-S., Going, S., & Chen, Z. (2015). High genetic-risk individuals benefit less from resistance exercise intervention. International Journal of Obesity (2005), 39(9), 1371–1375. https://doi.org/10.1038/ijo.2015.78
Leońska-Duniec, A., Ahmetov, I., & Zmijewski, P. (2016). Genetic variants influencing effectiveness of exercise training programmes in obesity – an overview of human studies. Biology of Sport, 33(3), 207–214. https://doi.org/10.5604/20831862.1201052
Ng, M. C. Y., & Bowden, D. W. (2013). Is Genetic Testing of Value in Predicting and Treating Obesity? North Carolina Medical Journal, 74(6), 530–533. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073883/
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