A little over a decade ago, spurred by the success of the humans Genome Project and the affordability genetic sequencing, scientists began to explore the promise of nutrigenomics. The big question was could personalized nutrition, informed by knowledge of a person DNA, help prevent and even treat diet related diseases?
the results of early studies from Stanford, Harvard and elsewhere were compelling: Genetic differences seemed to predispose individuals to lose weight on different types of diets. A big time multimillion dollar industry soon sprang up, premised on marketing DNA-based diets. However, subsequent research has failed to show any statistically significant difference in weight loss between overweight individuals who eat right for their genotype, and those who do not.
In fact my friends, the effect of genes on obesity has been very hard to tease out; there has been various studies that have put the figure at anywhere from 35% to 85%. Nutritionists have long observed that no one weight-loss strategy will work for everyone, and that individuals show striking differences in their responses to different diets. What, then, actually explains the large Variation in individual metabolism?
In 2018, Sarah Berry and Tim Spector, epidemiologists at King’s College, London, and Dr. Andrew Chan, of Harvard Medical School, began a journey for the answer. Their study, called Predict, is a comprehensive experiment to look at the responses to food by individuals. The results, presented in June 2019 at the American Society for Nutrition’s annual conference, documented, for the very first time, surprising variations in how well participants processed fats and carbohydrates, even among identical twins. How efficiently a person metabolized one macronutrient was no predictor of how that person might respond to another.
Dr. Eric Topol, stated, we are getting closer to being able to provide guidance for each person for what their ideal diet should be, Dr. Topol is a geneticist at the Scripps Research Translational Institute in La Jolla, California, who was not affected with the study. Dr. Spector has been exploring the causes of individual variation in disease risk, that include drug-related ailments. In 1992, he set up TwinsUK, then was a Research registrar that now includes more than 13,000 identical and fraternal twins. Based on the twins, Dr. Spector concluded that Gene’s contributed 70% of an individual’s risk for obesity, on average.
Intrigued, he began a series of studies to tease out which factors influenced the remaining 30%. In 2014, he began the British Gut project, a6 crowd sourced effort to better understand the diversity of Gut microbes, their response to different dietary interventions and their effects on weight. Among the registry of twins, he began to noticed, even identical pairs only shared about 50% of their Gut bacteria.
Dr. Spector then started Predict to explore how variations in individual responses to carbohydrates and fats might contribute to obesity. Eating foods that contain carbohydrates and fats causes glucose, insulin and triglyceride levels in the blood to rise and fall; spikes that are too high, and too frequent are associated with inflammation, diabetes, heart disease and weight gain.
The study included 700 identical twins, 100 subjects from the United states and 300 British volunteers, and gathered data on almost every letter thing that can affect metabolism: gut microbiota, exercise, sleep duration, body-fat composition and more. These initial results, however, analyzed only the rise and fall of glucose, insulin and triglyceride levels in the blood after participants had eaten standardized meals.
The team came to the conclusion that genes play a limited role in how a person process carbohydrates and fats. Among identical twins, only about half of the amount and duration of individual’s post-meal blood glucose level could be attributed to genetic influence, and less 30% with regard to insulin and triglyceride response. The more important factors in metabolizing food, it seems, are environmental: stress, sleep, exercise and the diversity of the gut microbiota.
This is really exciting for individual’s and scientists, says Dr Berry. It has shown all of us how much is not genetic and therefore modifiable. However Dr. Berry noted that the proportion of fats and carbohydrates in a meal explained less than 40% of an individual’s response to that food. That finding, reinforces the message that we should focus on the whole lifestyle approaches rather than individual foods and nutrients, she says.
The full data set will take Dr. Spector and his colleagues, which is some 40 scientists around the world, years to analyze, even with the help of machine learning. But it was already possible to glean individual insights, Dr. Spector said. After eating potato chips, one of the subjects repeatedly experienced a triglyceride level six times higher than of an identical twin. That degree of awareness could help steer the chip-sensitive twin to lower-fat snack, Dr. Spector said.
Jennie Brand-Miller, who is a professor of human nutrition at the university of Sydney in Australia, who was involved with Predict, said that individualized nutrition advice, rather than the standard dietary guidelines that are based on population-wide averages, could actually improve public health significantly. The Almighty one-size-fit -all nutrition guidelines is without a doubt antiquated, Dr. Miller said. She noted that one in three individuals has a poor metabolic response to sugar; identifying those individuals, and then teaching them how to avoid spikes in blood glucose, may reduce their odds of later developing diabetes by as much as 40%.
The standard nutrition guidelines are built on data from questionnaires, that ask people how frequently they ate certain foods in the past year. The approach provides some useful data about the overall trends, however, it is also flawed: Respondents are notoriously bad at remembering their food choices and average data cannot offer personalized guidance.
Recently a much more detailed view of our metabolic differences has come available, with the advent of affordable machine learning, wearable sensors and genetic sequencing. The result has been a big surge of interest in the field. In February 2019, another large-scale, multiyear personalized nutrition study was started at the Swiss Federal Institute of Technology, in Lausanne.
The basic parameters of a healthy diet are well-known already: plenty of pulses, whole grains, dark leafy greens and other vegetables, lots of seafood healthy oils and very little red meat or refined carbohydrates. The problem it’s not that the guidelines are actually wrong or insufficiently personalized, says Tim Caulfield, who researches health law and policy at the university of Alberta in Canada, but that people are not ready following them. It is a fantastically complex issue that has all to do with our built environment, with socioeconomics, with our food environment, with marketing, and with our activity levels-so many things, Caulfield said.
As a study, Predict is still in its early stage; whatever individualized recommendations it might provide, there is no real evidence yet that they can improve a personal health any more than standard additional guidelines can. Nonetheless, its scope and rigor are without a doubt novel. It will require further validation, and doesn’t equate with preventing cancer or heart disease or other outcomes, Dr. Topol said. But it is still very important if we are ever going to get to the ‘food as medicine’ ideal.
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