2 February 2020
Genetics and Fat Loss: Does genetic coding hold the secret to your personalised fat loss approach?
Genetic variants and fat loss: does your genetic code hold the secrets to your personalised fat loss approach? Personal genomics companies (23andMe, DNA Fit, Helix) have exploded onto the market in recent years, making big promises regarding “accurate” recommendations for diet, skin care, lifestyle as well as information on future medical risks, fitness and athletic…
Genetic variants and fat loss: does your genetic code hold the secrets to your personalised fat loss approach?
Personal genomics companies (23andMe, DNA Fit, Helix) have exploded onto the market in recent years, making big promises regarding “accurate” recommendations for diet, skin care, lifestyle as well as information on future medical risks, fitness and athletic abilities.
However, the FDA has halted the continuation of these services due to inadequate scientific evidence. Many seem to have slipped through the gaps though…and continue to operate.
In the context of our little fitness niche, the concept of personalised genome-based diet strategies, using modulation of nutrients and exercise optimised to the specific needs of a particular individual…is appealing to say the least.
Indeed, previous studies have shown that each individual has a genetic predisposition, that might benefit from a specific diet and exercise program…yet the gap between this information, and the information required for prescription in practice, is substantial.
Latest Research on Fat Loss Genetics
Recently a paper was published which set out to examine the interaction between genetic variation and changes in diet or exercise habits which influence fat loss, to try and illuminate this area of knowledge which is largely shadowed at present. Using a cohort size of 8840 individuals, 673 SNPs (single nucleotide polymorphisms = genetic variants) were identified to be somehow related to the progression of obesity. From this 673, 100 were identified to have gene-environment interactions (e.g. related to carbohydrate intake, fat intake, calorie intake and exercise).
Using their population of 8840, the researchers divided the participants into four levels of sensitivity (very sensitive, sensitive, insensitive, very insensitive) in response to carbohydrate intake, fat intake, total calorie intake and exercise, for fat loss. Upon analysing the results, body fat losses were larger when an individual had a greater sensitivity level to one of the environmental factors (carbs, fat, calories or exercise), suggesting that genetic variants do influence the effectiveness of a dietary regime for fat loss.
Overall, as the effectiveness level (or sensitivity level) increased from very low to very high, body fat reduction tended to be higher according to either reduction in nutrient intake categories, or exercise. For example, following a greater than 75g reduction in carbohydrate intake, body fat losses increased from 370g to 540g to 1190g in individuals classed as insensitive, sensitive or very sensitive, respectively (sensitivity to carb intake that is). Thus, these results suggest that an individual with a genetic predisposition (according to the SNPs selected in this study) classed as very sensitive to carbohydrate intake, may benefit more from reducing carbohydrate intake compared to other individuals classed as insensitive or very insensitive.
To summarise the findings from this paper, the identified SNPs may be functionally related to the metabolism of nutrients in the body. In fact, a number of the SNPs in this study have previously been reported to also be associated with metabolic syndrome or insulin resistance. However, it’s important to mention that there are still substantial limitations of this research. Firstly, given the potential inaccuracies in large cohort date, false positives (relationships appearing statistically significant when they aren’t) might be present in the selected SNP set. Nutrient and calorie intakes for the study were also based off food frequency questionnaires, meaning errors could have arisen from subjective judgement on the day of administering the questionnaire, or biased by the most recent memory of the participant’s eating behaviour. The question “please check if you do sweaty exercise regularly” also doesn’t differentiate on the varied degree of physical activity, such as low, intermediate or high intensity activities. Finally, it is not feasible to remember the exact number of days exercise was completed, and being that questionnaires were administered two years apart, unobserved environmental factors might have influenced body fat loss also.
To reach a point where this line of research could be usable in practice, the scientists have a little journey ahead of them. Instead of retrospective studies (looking backwards like this one), we need prospective experiments by grouping individuals into exercise or dietary groups which are further divided into either low carb or low-fat diet groups. We then can apply the SNP model to an independent data set, to see if findings are replicated, and if not update the algorithm for the SNP set that influences diet efficacy. Such a finding would ultimately provide an effective genome-based personalised diet or training regime that maximises effectiveness for a particular individual.
It’s extremely realistic to expect that genome-based tailored nutrition and exercise programs will be the most significant revolutionary advancement in the fitness community during the 21stcentury. If you want to be ahead of the curve, I’d be keeping your finger on the pulse of this research.
Cha et al., 2018. Nutrients. Impact of Genetic Variants on the Individual Potential for Body Fat Loss