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About This Episode:
"Personalized nutrition" has been promoted as an approach that will improve people's health by prescribing them specific dietary recommendations based on their own genetic and phenotypic data.
The premise is that given we each respond differently to foods, having general dietary recommendations may be doing many people a disservice. And by using an array of personal data, it is now possible to give unique diets that improve health.
The early and interesting findings of research in this area was met with much fanfare, and indeed, many companies are now offering commercial direct-to-consumer services based on genetic and physiological testing, followed by "personalized" dietary prescription. Such testing may include genetic tests, microbiome testing, glucose monitoring data, and more. This data is then fed into machine learning algorithms to prescribe dietary recommendations.
However, do the marketing claims match the current evidence? Does the "proof" it works that is often cited, actually back up the claims? Do personalized nutrition diets actually lead to improved health outcomes over generic, conventional dietary recommendations? Do personalized nutrition diets lead to better outcomes than standard dietetic/nutrition practice?
To answer these questions, we go through the main studies cited in favor of personalized nutrition being superior to typical dietary advice, and see if they indeed support the claims.
So is personalized nutrition superior to standard dietary advice? Let's find out…
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