Gut reaction

I came across a 2015 paper written by David Zeevi and his colleagues this week.
The paper is titled Personalized Nutrition by Prediction of Glycemic Responses. Their graphical abstract is:

I had a profound, positive gut reaction to a paper about the gut. I think there is a lot to admire in this paper and I believe it has an important role to play in how we might present our research in sport as we explore how we might use analytics to transform performance.
Their brief abstract is:

People eating identical meals present high variability in post-meal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower post-meal blood glucose and its long-term metabolic consequences.

I was particularly interested in how the research team analysed the data from their participant cohort:

We asked whether clinical and microbiome factors could be integrated into an algorithm that predicts individualized postprandial (post-meal) glycemic responses (PPGRs). To this end, we employed a two-phase approach. In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants. In the second, validation phase, an independent cohort of 100 participants was recruited and profiled, and their PPGRs were predicted using the model trained only on the main cohort.

In their discussion, David and his colleagues note:

In this work we measured 46,898 PPGRs to meals in a population-based cohort of 800 participants. We demonstrate that PPGRs are highly variable across individuals even when they consume the same standardized meals. We further show that an algorithm that integrates clinical and microbiome features can accurately predict personalized PPGRs to complex, real-life meals even in a second independently collected validation cohort of 100 participants. Finally, personalized dietary interventions based on this algorithm induced lower PPGRs and were accompanied by consistent gut microbiota alterations.

The conclusion to the paper includes the following observation:

Dietary interventions based on our predictor showed significant improvements in multiple aspects of glucose metabolism, including lower PPGRs and lower fluctuations in blood glucose levels within a short 1-week intervention period. It will be interesting to evaluate the utility of such personalized intervention over prolonged periods of several months and even years. (My emphasis)

I do think this is an exemplary paper. I found the language and transparency of their narrative very engaging. There were some powerful visualisations to add to my engagement. Their use of machine learning has sent me off thinking about the generalisability of their approach.
Above all, I was left thinking about “the utility of such personalized intervention over prolonged periods of several months and even years” in a wider sense of personal learning and performance journeys. David and his colleagues have shone a microbiota light on how I might do this.


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