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by Wei Perng, PhD


Advancements in high-throughput technologies have enabled us to assess health from a more holistic point of view by considering our genetic code (“genomics”), actual expression of our genes (“epigenomics” and “transcriptomics”), the structure of proteins that carry out the biological processes (“proteomics”), and the unique chemical fingerprints that reflect our physiological response to external and internal conditions (“metabolomics”). Of particular interest to an obesity-prevention researcher is the possibility of “omics” to influence nutrition counseling, a one-on-one process between a patient and a nutritionist that aims to help the patient make and maintain dietary changes necessary for good health. These recommendations have historically been based on dietary recommendations derived from nutrient needs of heterogeneous populations. Enter the concept of personalized nutrition - a diet plan designed at the level of the individual, tailored to meet their specific health needs.
But first, let’s take a step back to think about why this makes any sense at all. Anecdotally, we all know someone who can seemingly eat whatever they want and not gain a single ounce. Conversely, there are others who eat healthfully, exercise regularly, and have trouble staving off excess weight. Why would this be? We now know that a major determinant of weight gain is glycemic control, or how well we manage our blood sugar levels. When we eat, our blood sugar levels rise. The speed at which blood sugar peaks and how quickly our body is able to get it back to normal is very important to our health. Constant spikes in blood sugar (or worse, consistently elevated blood sugar levels) not only leads to storage of sugar as fat, but it also increases risk of chronic diseases like type 2 diabetes. Therefore, nutritionists have traditionally classified foods using the glycemic index, a metric for how much of a blood sugar spike a food elicits. High glycemic index (GI) foods, such as those high in  refined flour and sugar (e.g. white bread, pasta noodles, cake) are typically thought of as being bad for our health, while low GI foods are considered good (e.g. whole grains, most fruits, non-starchy vegetables).

However, according to a study led by Drs. Eran Segal and Eran Elinav at the Weizmann Institute of Science called “The Personalized Nutrition Project,” whether a food is high vs. low GI depends on the person. In a recent BBC article, Dr. Saleyha Ashan provided a participant’s point-of-view of the study. During the week-long study period, Ashan received a glucose monitor implant to monitor blood sugar levels in response to standardized meals over the course of a week. She was paired with another female participant, with whom she ate exactly the same meals for 6 days. At the end of the week, Ashan discovered that her favorite healthy snacks, like grapes and sushi, elicited major blood sugar spikes while bad foods like chocolate and ice cream did not. Her study partner’s results were the exact opposite. What could explain these differences?

Along with the blood sugar assessments, participants also provided a stool sample, which was analyzed to unveil the composition of gut microbes, also known as the gut microbiome. As discussed in a previous post, our gut microbiome influences many aspects of health, including metabolism. Segal and Elinav suspect that the type of microbes in our guts holds the key to understanding why certain foods cause elevations in blood sugar for some but not others. So far, it seems that healthier people have a broader diversity of gut bacteria. What is intriguing (and relieving) is that unlike our genes, our gut microbiome is potentially modifiable. The second phase of the Personalized Nutrition Project involves formulation of a diet plan for each participant based on findings from the blood sugar monitoring phase. Ultimately, Segal and Elinav hope to be able to develop a personalized diet to stabilize blood sugar levels for anyone via a single analysis of their stool sample.

However, as discussed in a recent paper, the road to developing personalized nutrition may be bumpy. Additional research is required to understand which foods correspond to which gut microbes, whether altering specific microbes is effective to modify physiological responses to food intake, and whether these relationships differ by age, race/ethnicity, and sex. Ultimately, we must also consider whether the cost-to-benefit ratio of personalized nutrition is indeed an improvement over more general recommendations to limit consumption of unhealthy fats, sugars, and refined grains, and to increase intake of fruits, veggies, and healthy fats.


 


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