One of the benefits of Supersapiens partnering with Abbott to bring the Libre Sense Glucose Sport Biosensor to the market is that continuous glucose monitoring (CGM) is now available to the public and researchers for use in people without diabetes. Additionally, the Supersapiens system offers a higher level of precision and much improved user experience and interface than previously available.
This has coincided with significant increases in research from a CGM in non-diabetic populations perspective, both at the level of the individual and in scientific settings. This may well prove helpful to those with diabetes ultimately, but if nothing else it is illuminating glucose from a different perspective and challenging some assumptions around glucose regulation in those without diabetes.
The advent of technologies like CGM, and availability to the individuals has meant that a level of self and small scale experimentation can occur. This has yielded a more informed set of individuals in the population, answering questions for themselves rather than having to rely on science that may be less representative of their own physiology because of the potential large inter-individual differences (glucose is a particularly individual analyte). Similarly, it has facilitated quicker, easier small scale proof of concept type of studies to be done, to help improve larger scale studies. Some of these have been done for internal validity and testing purposes or even just internal learning for companies.
There are many ways to approach using Supersapiens, but the users we see having the most success approach things with a scientific lens.
They run mini experiments and generate hypotheses about how their body will react, evaluating these and then iterating again.
The best way to execute this is by starting with the basics and understanding group and population norms. That is, use the research and scientific consensus to understand the general impact of something at the population level.
Once you understand the effect something may have on a population, you then know that in general this should hold true to that population (though there are often some outliers). From this point, it is about understanding the individual in the population, YOU.
The fact that glucose is impacted by so many factors can make understanding glucose responses to any one intervention quite difficult. There are two approaches to this problem; limit as many variables as possible to limit their impact and understand the intervention you are interested in. The alternative is to use a more ‘free-living’ type of experimentation, not limiting many variables and trying to understand the impact of the intervention as part of the milieu of factors impacting glucose. This latter approach may be likened to trying to hear a single instrument in a symphony. It can be done, but it requires meticulous attention, notes and a large volume of trials (or in the analogy; listening to the symphony over and over again). This allows you to start seeing some ‘signal’ of the impact of the intervention through the ‘noise’ of all the other factors impacting glucose.
An example of this process can be seen in some work done by Caitlin Hall, a dietitian and the head of clinical research at Myota. Knowing that glucose responses are reduced by fibre, she started experimenting with some meals and kindly shared her experience and learning whilst using Supersapiens.
As mentioned, the advent of technology like CGM which is available in free living conditions has opened the door for science to be done at different scales and levels. For companies, this may speed up the R&D process, or allow for validation and testing to be done on cheaper and smaller scales. Similarly, it may aid in proof of concept for researchers or even more field based studies.
This is exemplified in recent work from Myota (a dietary fibre blend), who looked to investigate one of their products, the ‘metabolic regulator’.
What they Did:
They started with 30 people, though only 27 people completed all of the trials.
- Participants ate 2 different meals (toast and cornflakes), 3 times each.
- The 3 trials per meal were; adding nothing, adding 10g of myota and adding 20g of myota.
- They used Supersapiens to gauge the responses to the two different meals and adding differing amounts of myota.
What they Found:
Despite what the data above looks like, the individual responses were very different. As Myota noted:
“There was huge variability between individual glucose responses. While some people had no difference when adding fibre, others had significantly lower peak glucose responses to fibre. This might be due to a number of factors, such as genetics, physical activity, amount of fibre, protein or fat consumed in the previous meal, or quality of sleep the night before.”
Following this, Myota in conjunction with the NHS, are expecting some more formal, clinical research results on the impact of Myota on glucose in those with pre-diabetes and diabetes in 2023.
This example speaks volumes to the various uses for technologies like CGM and different levels where the impacts can be relevant. On the individual level, even small scale pilot studies such as the one Myota ran may not be that relevant due to varying glucotypes. That said they inform the potential for further research or may validate products internally.
Similarly the need for large scale clinical research is real. This research aids greatly in informing understanding of the scientific and medical communities in addition to providing important starting points and knowledge for those interested in their own glucose. Superaspiens is very proud to be part of research on multiple levels; the individual for our users, the small scale for studies like the above and more formal research.
If you are a researcher and interested in using Supersapiens for your research please reach out for a discussion with our science team.
- Holdcroft A. Gender bias in research: how does it affect evidence based medicine? J R Soc Med. 2007 Jan;100(1):2-3. doi: 10.1177/014107680710000102. PMID: 17197669; PMCID: PMC1761670.
- Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, et al. (2018) Glucotypes reveal new patterns of glucose dysregulation. PLOS Biology 16(7): e2005143. https://doi.org/10.1371/journal.pbio.2005143