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It is impossible to outrun the fork

The Pareto Principle is also applicable to a healthy lifestyle change, regarding nutrition and fitness. The Pareto Principle is the 80/20 rule.

We see this in business. 80% of business comes from 20% of customers. We see it in work days’ productivity. 80% of our work yield comes from 20% of our work time. We see this in almost all aspects of our lives. We must also heed this ratio on our journey to fat loss.

It is not scientifically proven that this is correct, but most scientific studies prove it to be nearly correct.

So, the pros of fitness, I will describe for you. We believe in fitness, because science has proven that number one, it is a mood elevator. Also, it builds muscle, which is extremely important to burning calories. Nutrition alone will not do it. Because we tend to lose our muscle cells, when we tend to reduce our proper macronutrient caloric levels; nor do we augment any new essential muscle cells to burn calories during our fat loss stage and maintenance stage. Thereby, we do not embark on a true healthy lifestyle change. We don’t have to go out and join a gym. We need to buy some good walking tennis shoes and some economical weights from our local sporting goods stores.

If your genetic report shows that you are not a marathon runner and you are more likely to lose fat with resistance training, then we still recommend some walking exercises. Maybe twenty minutes of walking and forty minutes of doing some resistance training with your new weights.

The point that I would like to make in the aforesaid, is that nutrition is our fuel and physical activities are our turbochargers. We need them both. But, let’s not put so much emphasis on physical activities and less on nutrition.

I was walking two weeks ago, with a friend of mine, and he said that he cheated and ate two donuts for breakfast. Chuckling, he told me that he had to walk another hour that day. I don’t believe that we should deprive ourselves of one of our favorite sweets, once a month, or even every two weeks. I didn’t know if it was a plain, glazed or chocolate-filled donuts. I dare to say that he may have had to walk six to seven more hours that day to burn those extra calories.

This is very common thinking about people trying to lose fat and it’s role with physical activity. There are also metabolic issues in play, which I am not even going to touch right now, i.e., insulin levels, etc.

We believe that nutrition comes first, then physical activity, but something almost as important is changing eating behavior and proper sleep.

So, hopefully, the takeaway from this little blog article is that we extricate our mindset that physical activity is most important, but take nutrition and eating behaviors into the fat loss equation toward achieving and maintaining a healthy lifestyle.

In closing, I will say that I am not close in being knowledgeable in kinesiology whatsoever, but we cannot ‘outrun the fork’, nor for that matter, a donut!


Hedonistic obesity – the driver of disordered eating behavior?

Our eating behavior is driven by a physiological need to satisfy hunger and provide energy, as well as a neurological desire to consume foods we want or like. These drivers are common to al humankind, however, differences in BMI and the obesity epidemic show that some respond to these needs and desires differently to others.

Many experts agree that we all have a weight ‘set-point’ which is largely determined by our genetics and of which should be kept relatively constant by an ‘energy homeostasis system’ including the hypothalamus, appetite hormones and digestive system to regulate appetite and food intake according to our physical needs (Yu, 2015) The rise of obesity, however, suggests that one is able to override their weight set-point, and researchers are investigating the role of the brain driving eating behavior beyond physical needs and leading to what is being called ‘hedonistic obesity’.

The study of hedonistic obesity looks at the neuronal pathways involved in the reward system of the brain. Through functional MRI (fMRI) scans, images have shown higher activity in the reward regions of the brain in response to food cues among obese individuals, compared to healthy weight controls (Stice, 2008).

Recently emerging is the role of genetics in susceptibility to hedonistic obesity. Commonly known variants such as; FTO and MTHFR, play a significant role in the hypothalamus and can therefore be argued as highly influential to eating behavior. Furthermore, genetic variants for taste receptors and appetite hormones may also play an important role in individuals ‘liking’ and ‘wanting’ food beyond their physical satiety signals (Grimm, 2011)

A recent study on school children used fMRI to measure the response of food stimuli in the reward-related area of the brain – nucleus accumbency (NAcc) and correlated their findings with BMI and the obesity risk polymorphism FTO rs9939609. It was found that those with the risk ‘A’ allele had a larger NAcc volume and also showed higher brain activity in response to being shown a food commercial in a controlled environment. The study concluded that risk of hedonistic obesity may be apparent from a very early age dependent on genetic variants affecting the development and growth of reward-related regions of the brain (Rapuano, 2017).

Also interesting is the correlation between low levels of dopamine and dopamine receptors and obesity. It has been suggested that those affected may display more frequent eating behavior and likely to eat larger portions in order to receive reward signals similar to those with normal dopamine levels (Grimm,2011). Whilst nutritional neuroscience is growing, so is the genetic influence to such, and this is therefore a key area for health professionals to watch. If we can begin to understand the underlying mechanism which dictate eating behavior, then we are much closer to understanding how to tackle hedonistic obesity in susceptible individuals.

As a practitioner, you might want to get further insight on environmental triggers and ask questions such as;

“where do you eat your meals when at home?”

“How do you spend your lunch time?”

“Describe how you would spend family time with your children”

“Do you feel satisfied after finishing your meal?”

Looking into the future, nutritional neuroscience will play a bigger role in the treatment plans as we learn more about the brain and neuronal pathways. By knowing which genotype clients have inherited, this may provide clues as to the best nutritional intervention strategy to employ. We are certainly watching this area closely!


The GenoVive Team




Felsted, J. A.-D. (2010). Genetically determined differences in brain response to a primary food

reward. Journal of Neuroscience,30(7): 2428-32.

Grimm, E. R. (2011). Genetics of Eating Behavior: Established and Emerging Concepts. Nutrition

Reviews, 69(1): 52-60.

Rapuano, K.M.-D. (2017). Genetic risk for obesity predicts nucleus accumbens size and

responsivity to real-world food cues. PNAS, 114(1)160-165.

Stice, E.S. (2008). Relation between obesity and blunted striatal response to food is moderated

by TaqIA A1 allele. Science, 322(5900): 449-452.

Stice, E.S. (2008). Relation of reward from food intake and anticipated food intake to obesity; a

functional magnetic resonance imaging study. Journal of Abnormal Psychology, 117(4):


Yu, Y.-H. V. (2015). Metabolic vs. hedonic obesity: a conceptual distinction and its clinical

Implications. Obesity Reviews, 16: 234-247.


Exercise for mood, health and weight management

Brain-derived neurotrophic factor (BDNF) is a growth factor found abundantly in the brain and is known to play a key role in neuronal cell survival, synaptic plasticity and synaptogenesis (Wrann, 2013). Particularly relevant is its function in the hippocampus which is an area of the brain responsible for learning and development, therefore, studies have looked into the association of cognitive abilities and BDNF plasma or serum levels (Zoladz, 2010).

It has been shown that higher levels of plasma or serum BDNF correlates with good brain function, positive mood and better memory performance which has led to studies looking at how BDNF levels are increased (Gomez-Pinilla, 2013). Of interest is the exercise induced increase of BDNF expression which has been consistently found in rodent studies and now human studies are beginning to replicate findings (Zoladz, 2010).

For example, a year-long study of 120 older adults split into two groups of aerobic exercise and stretching (control group) measured and compared the hippocampal volume, BDNF levels and memory function between the groups. It was found that those in the aerobic group had a 2% increase in hippocampal volume by the end of the study. This was correlated with increased BDNF levels and also better memory performance (Erickson KI, 2011).

Of further interest is the finding of a BDNF polymorphism Val66Met which has been associated with poorer cognitive function and memory (Zoladz, 2010). A study with a relatively impressive sample size of 205 elderly participants looked at the influence of the Met polymorphism on physical activity and memory. As expected, it was found that the Met polymorphism was associated with a significantly decreased performance of episodic memory (Canivet, 2015).

This is relevant to practitioners as we begin to understand more of why exercise is beneficial to the brain and general health. In terms of reducing the risk of cognitive diseases such as; dementia and Alzheimer’s as well as improving mood and well-being for those on a weight-reduction plan, it may be that regular exercise helps to maintain good levels of BDNF expression and therefore improve cognitive performance.

Therefore, exercise is for more than just losing weight or for those wanting to improve their fitness levels, it has benefits that go way further in terms of gene expression and longevity hence practice daily. Brisk walk anyone?

The Genovive team



Canivet, A. A.-B. (2015). Effects of BDNF polymorphism and physical activity on episodic memory in the elderly: a cross sectional study. European Review of Aging and Physical Activity, 12: 15.

Erickson KI, V. M. (2011). Exercise training increases size of hippocampus and improves memory . Proceedings of the National Academy of Sciences of the United States of America, 108(7):301-3022.

Gomez-Pinilla, F. H. (2013). The Influence of Exercise on Cognitive Abilities. Comprehensive Physiology, 3(1)403-428.

Wrann, C. D.-B. (2013). Exercise Induces Hippocampal BDNF through a PGC-1α/FNDC5 Pathway. Cell Metabolism, 18(5):649-659.



Have yourself a merry mindful Christmas…

The festive season has begun and so too the busy social calendar with work parties, Christmas shindigs and of course the family get-togethers. So too, therefore, the boxes of chocolates, the turkey dinners, the mince pies, the Christmas pudding, the cheese, the mulled wine, the list goes on…

Although aimed to be a time of joy and cheer, unfortunately it can also be a time of heightened anxiety for clients who have spent so much time reducing their intake and working towards their weight loss goal. The projection of being surrounded by so much ‘temptation’ and larger than life portion sizes can be a source of fear of ‘losing control’ and ‘putting all the weight they lost back on’. So as practitioners, how can we support clients to find a balance between enjoying the festive season and food on offer whilst not undoing their hard work and bringing them back to the start of their journey?

The answer may be in introducing the client to Mindful Eating. Originating from Buddhist practice as one being in a state of acute awareness to the present moment with no critical judgements, translates to eating with a mindful approach whereby the appearance, smell, texture and taste of food are taken into detailed consideration to truly ‘savour’ the moment in a non-critical way (Kabat-Zinn, 2003).

Studies have shown that using this approach to help with overeating and obesity significantly improves outcomes. For example; a study looking at the association between mindfulness treatment and abdominal obesity found that participants in the study group who showed improvements in using mindful techniques also showed the most weight loss in abdominal fat (Daubenmier, 2011). Furthermore, a mindfulness intervention has been shown to reduce episodes of binge eating and associated anxiety in participants (Smith, 2006).

If this approach sounds like it could be of benefit to preparing your clients for Christmas, there are several tools available to use in clinic. A questionnaire to identify clients’ current mindful awareness is available (Framson, 2009). Moving on from that, conducting a mindful eating exercise in clinic can also demonstrate the concept itself to the client and show how it can reduce overeating. Although traditionally a raisin is described, this can be adapted to be a food your client particularly struggles eating moderate amounts of, or for a festive flavour try a minced pie (Extension Service West Virginia University, 2016).

Using this approach and practicing it in clinic may be the coaching your client needs to truly enjoy the festive season and all the social gatherings and food around it, whilst continuing their healthy lifestyle and weight loss goals.


Merry Christmas all!

The Genovive team



Daubenmier, J. K. (2011). Mindfulness Intervention for Stress Eating to Reduce Cortisol and Abdominal Fat among Overweight and Obese Women: An Exploratory Randomized Controlled Study. Journal of Obesity, doi:10.1155/2011/651936.

Extension Service West Virginia University. (2016, December 3). Eating One Raisin: A First Taste of Mindfulness. Retrieved from West Virginia University:

Framson, C. K. (2009). Development and Validation of the Mindful Eating Questionnaire. Journal of the American Dietetic Association, 109(8)1439-1444.

Kabat-Zinn, J. (2003). Mindfulness-Based Interventions in Context: Past, Present, and Future. Clinical Psychology: Science and Practice, 10(2)144-156.

Smith, B. W. (2006). A Preliminary Study of the Effects of a Modified Mindfulness Intervention on Binge Eating. Journal of Evidence-Based Complementary & Alternative Medicine, 11(3)133-143.



Is your client destined to like fatty food?

The fight against obesity continues. All the while, health professionals and researchers continue to explore the cause and the prevention of obesity to reduce the negative impact on society and individuals alike.

Genetic studies have begun to unpick how DNA, in conjunction with environmental stimuli may be a key driver in BMI and eating behaviours. This connection is rapidly growing and recent studies may have identified a genetic disposition to preferring food with a high fat content. Could this mean, therefore, that some possess a subconscious preference for high fat, therefore, high calorie food – putting them at an increased risk of obesity?

Specifically, genetic variants in melanocortin-4 receptor (MC4R) and CD36 have gained attention for possibly providing a fatty acid preference in individuals.

A link between MC4R – found in the hypothalamus – and appetite regulation is well founded (Z Yilmaz, 2015). Genetic studies have clearly shown an association between some variants and increased BMI – suggesting possible alterations in appetite regulation causing weight gain (Loos RJ, 2008). Although concrete mechanisms have not been established, a recent study hitting the headlines (Wanjeck, 2016) found that carriers of a MC4R polymorphism consumed 95% more of a Chicken korma dish with a higher fat content compared to non-carriers. Two dishes identical in taste, texture and appearance but lower in fat content were consumed at a lower quantity in the MC4R variant carriers (Agatha A. van der Klaauw, 2016). The first study of its kind in human subjects, suggests that genetics may in part explain a preference for fatty foods and therefore an increased risk of obesity.

Other research has investigated variants of the gene CD36 – a fatty acid translocase expressed in many sites, including taste cells, and possessing a functional role in fatty acid metabolism (Kathleen L. Keller, 2012). Studies suggest that it may also influence fat preference in carriers, and therefore BMI. A relatively small study of African American obese subjects found that variants of CD36 noted a high fat salad cream as ‘creamier’ than a lower-fat version – suggesting an increased sensitivity to fatty acids (Kathleen L. Keller, 2012). Interestingly, however, linking this to a higher BMI has not been conclusive (Dongli Liu, 2016). It has been noted that a hypersensitivity to fatty acids may enable individuals to consumer a lower amount of that food (Kathleen L. Keller, 2012).

Although research continues to grow, such information contributes to the idea of personalised nutrition. We might be just that bit closer to identify individuals with a higher affinity for fatty foods  which could help you tailor advice to your clients. Do clients with a preference to fatty foods therefore consume too much and require coaching to reduce intake, or in fact does this mean they could be supported to switch the type of fats they are consuming? Knowing which genotype your client has inherited could steer your approach in the right direction and empower them in turn with useful and actionable information.



Agatha A. van der Klaauw, J. M. (2016). Divergent effects of central melanocortin signalling on fat and sucrose preference in humans. Nature communications, DOI: 10.1038/ncomms13055.

Dongli Liu, N. A. (2016). Mechanism of fat taste perception: Association with diet and obesity. Progress in Lipid Research, 63: 41-49.

Kathleen L. Keller, L. C. (2012). Common Variants in the CD36 Gene Are Associated With Oral Fat Perception, Fat Preferences, and Obesity in African Americans. Obesity (Silver Spring), 20(5): 1066–1073.

Loos RJ, L. C. (2008). Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genetics, 40(6): 768–775.

Wanjeck, C. (2016, October 4). Like Fatty Foods? There’s a Gene for That. Retrieved from Live Science:

Z Yilmaz, C. D. (2015). Association between MC4R rs17782313 polymorphism and overeating behaviors. International Journal of Obesity, 39, 114–120.




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