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Friday, 20 November 2015

Nutrition is personal. Identical foods produce “healthy” and “unhealthy” responses in different individuals.

In today’s issue of Cell, two groups lead by Eran Elinav and Eran Segal have presented a stunning paper providing startling new insight into the personal nature of nutrition.The Israeli research teams have demonstrated that there exists a high degree of variability in the responses of different individuals to identical meals, and through the elegant application of machine learning have provided insight into the diverse factors underlying this variability.

As genetic factors are known to modulate an individual’s innate responses to diseases, medications, and blood metabolites, it may come as no surprise that individuals do not respond to identical foods in the same manner.

Following a meal, glucose levels increase according to the type of foods that are ingested, and currently meal carbohydrate or derived glycemic index are used to estimate the postprandial (post-meal) glycemic responses (PPGR). These factors assume that PPGRs are solely dependent on the intrinsic properties of the ingested food, and this assumption is the basis of universal dietary recommendations.

While it is has been proposed that individual differences in PPGR may be influenced by diverse factors included genetics, lifestyle, and insulin sensitivity, as well as the activity levels of exocrine pancreatic and glucose transporters, the influence of gut microbiota on PPGR is relatively poorly understood.

In order to better understand these relationships, the Elinav and Segal teams sought to quantify individual PPGRs for a sample population, characterize the variability within their sample, and then identify factors associated with that variability. Though this could be achieved with a small set of a dozen, or perhaps a few dozen participants, this is not the approach that the teams took.

In the work they report today, Elinav and Segal report on their observations of a cohort of 800 healthy and pre-diabetic individuals, a sample population representative of the larger Western non-diabetic population.

Each member of the sample population was connected with a continuous glucose monitor which measure their interstitial glucose levels every 5 min for a full week (using subcutaneous sensors) collecting more than 2,000 measurements per participant, for a total of more than 1.6 million measurements for the entire population

In order to better understand the relationship between the measured glucose levels, and the unique physiologies and lifestyles of their study’s participants, the teams collected an extremely diverse set of information from each participant. Study participants kept diaries of their physical activity, food intake, and sleep using a smart-phone application; this information was then complimented with comprehensive profiles researchers collected from each study participant, including food frequency, lifestyle, medical background, anthropometric measures (body measurements, such as height, hip circumference), full panel blood tests results, and single stool sample results used for microbiota profiling.

When the researchers analyzed their collected results their findings varied from those that were expected, to those that were truly startling. As expected, the researchers were able to validate known associations of PPGRs with risk factors such as BMI, glycated hemoglobinn, morning glucose levels, and age. Within these associations the scientists did make some surprising observations, noting that these associations were not limited to extreme values; associations between these known risk factors occurred over the entire phenotypic spectrum indicating that incremental differences in the glucose response may be clinically relevant for some risk factors.

The collected observations further revealed both that an individual’s responses to the same food were reproducible, and that there exists a high levels of variability in the responses of different individuals to the same foods. The researchers found that the food associated with an individual’s highest glucose response varied greatly between individuals. Foods that induced a “healthy” response in one individual might induce an “unhealthy” response in another. In a particularly compelling figure, the researchers showed an example where two participants had opposite responses to cookies and bananas.

Using their set of amassed data, the researchers then went a step further, applying machine-learning algorithm to their cohort of 800 participants and developing an algorithm capable of predicting individualized PPGRs. This intricate algorithm incorporates 137 features representing meal content, daily activity, blood parameters, CGM-derived features, questionnaires, and microbiome features. This model predicts measured PPGRs with a significantly higher correlation (R=0.68) than solely relying on carbohydrate counting (R=0.38) or the meal caloric input (R=0.33).

Going even farther, the research team then recruited a separate 100 participant cohort and validated the predictive capacity of their algorithm. Finally, revealing the true utility of their approach a final set of participants was recruited for an intervention study. The intervention study used a set of 26 participants, distributed into two experimental groups, one that would apply predictions developed by an expert dietitian and researcher working together using continuous glucose monitoring data, and one that would use the algorithm the research team developed. Each participant was then assigned, in a doubly blind manner and in a random order, a one week long good diet, and a one week long bad diet.

The results of the intervention study revealed that the diets developed by both the expert dietician/researcher team and the algorithm resulted in lower blood glucose variability on the good diet, and the results indicated that the two methods had similar efficacy. Impressively, daily microbiome samples from these participants revealed that even short-term dietary interventions induced changes in the microbiome. The “good” diet was consistent with an increase in the beneficial bacteria, with a reciprocal decrease following the “bad” diet.

Link to full paper here:

Looks like the final nail in the coffin of Low GI diets 


1 comment:

chris c said...

"We are all different" but not in the way the low carb antis mean. Fruit seems to be the most variable thing for diabetics, many, like me, do best on berries and worst on bananas, but a few have the exact opposite result. Hardly any do well on low fat diets which is not surprising. Someone pointed out that your gut bacteria actually weigh more than your brain. Probably this explains certain trolls. It's not only your genes but the bacteria's genes that are important. Feed the internal world, and then TEST the results to tune your own diet.