We still don't see clear evidence that personalized diets provide better results than generic healthy diets, and these latest costly services might distract us from dealing with the "nutrition elephant in the room," notes NutraIngredients.

A critical analysis

In a webinar hosted by the European Nutrition Leadership Platform (ENLP), Dr. Nicola Guess, an academic dietitian and researcher specializing in dietary prevention and type 2 diabetes management, provided a critical analysis of the current evidence base for personalized nutrition interventions.

She explained that there are three key ways to provide personalized nutrition: through preferences, phenotype (observable health traits), or omics (data about the genome, microbiome, and metabolome).

Dr. Guess argued that there is a lot of evidence showing that if you personalize a diet to fit a person's lifestyle and preferences, it will help them adhere to the diet and lead to better health outcomes.

She also noted the DASH study which showed that diet can be personalized based on phenotype to lower blood pressure.

Furthermore, she believes that nutrition apps and artificial intelligence can be extremely useful in helping people stick to their nutrition plans and make healthy choices.

However, she questions the value of collecting omics data to provide dietary advice.

A marketing gimmick

Highlighting the massive investments in research seeking to demonstrate the benefits of omics approaches, Guess pointed out that there are already many commercial products on the market.

She said these companies generally market themselves on the claim that everyone needs a diet as unique as them.

"These services get billions of data points and use machine learning and artificial intelligence – it's seen as cutting edge and it's big news."

Discussing the validity of basing nutritional recommendations on omics data, Dr. Guess noted that one must consider whether analyzing an individual's omics data actually leads to personalized unique recommendations and whether prescribing these diets actually results in better outcomes compared to a healthy standard diet.

To find this out, she led viewers through some of the lesser-discussed aspects of some of the published personalized nutrition studies.

She argued that if you look at the algorithms created by a CGI-based personalized nutrition service, the algorithm will generally lead to a low-carb diet with more protein and fat.

"We know that high-protein and low-carb diets lower blood sugar spikes in anyone."

Looking at the algorithm for a service that takes urine, plasma, serum, saliva samples, and more, to cluster people into health categories, she said the algorithm created advice for a fairly standard healthy diet but with increased fiber.

"These data do not suggest the diets are personalized."

She noted that her own Preventomics study concluded that personalized meal plans did not lead to greater benefits than a generic healthy diet in the 10-week clinical trial.

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Type 1 error

She argued that omics-based services will collect billions of data points and with that, they are bound to discover statistically significant correlations.

"But correlations don't necessarily mean anything. None of them are necessarily causal. You can easily find statistical significance. But the question is whether it's clinically meaningful. Looking at glucose as a primary outcome, there is massive inter-individual variation in responses to glucose."

She noted that an individual's glucose response to a meal will be heavily influenced by the meal they had before and will be heavily influenced by the exercise they did earlier that day, the day before, and even the day before that.

"You can bring the same person back a second time and find completely different responses to the same foods. That's why it's so difficult to use CGM to find out what foods should be avoided. If you want to control intra-person variation, you should give the same meal at least three times. But as far as I know, these studies don't do that."

Additionally, she noted that blood and saliva samples only provide a snapshot of health, which is not helpful.

Control groups

She argued that another issue in personalized nutrition studies is when the control group of the healthy standard diet is given an inferior plan in terms of the amount of support and guidance they receive throughout the process.

"Regular contact will help someone make healthy choices and they'll be more likely to get results."

She suggested that if the personalization group receives an app with regular meal suggestions and prompts to make healthy choices, then the control group should also be given that, but with the standard advice regarding a healthy diet.

"If you're going to encourage people to spend a lot of money on your app, suggesting that your diet will be more effective than a standard healthy diet, this should be demonstrated with careful control."

Dangerous Distraction

Not only does she question the validity of the methodologies used and the marketing messages of these costly services, but she is concerned that there's a risk to overall health.

She highlighted the huge issue of excessive consumption of ultra-processed foods, with easy access to cheap undesirable food, making it easier for people to make poor choices.

"There's a big nanny state focus – nobody wants to regulate or tax food – so someone coming along and saying 'hey, just because people aren't eating the right food for their own metabolism' might be very attractive to people who don't want to address the nutrition elephant in the room."

Responding to an audience question about the potential of CGMs to trigger unhealthy choices and glucose phobia in the healthy population, Dr. Guess stated that high cholesterol and high blood pressure are two extremely widespread issues, whereas glucose control isn't a problem in the non-diabetic/diabetic population, so she doesn't see benefits in focusing on glucose.