We never guess, we always test

Our data analysis is based on the latest research and is always updated

How we assess your glucose compared to the general population trends

Based on data from [Hall 2018] we assess how your glucose compares to the rest of the population which uses the golden standard (OGTTs)

This is also very relevant. It shows Why the measurements we find of Glycemic Index are not enough (because they are averaged over a population) and thus do not work on an individual level, in other words, different people have different responses

More than 90% of the study participants agreed that the CGM sensor and receiver were easy to use (28/30 and 27/30, respectively), useful (28/30 and 29/30, respectively), and provided relevant information that was of interest to them:

  • [Lia 2018] Acceptability of Continuous Glucose Monitoring in Free-Living Healthy Individuals: Implications for the Use of Wearable Biosensors in Diet and Physical Activity Research

Note: the study above might have a selection bias problem in selecting the individuals


On the increased risk of outcomes (malignancies, CVD, diabetes etc.)

Participants who had larger glycemic variability had a significantly higher risk of malignancies:

  • [Kobayashi 2019] Glycemic variability and subsequent malignancies among the population without diabetes

Higher glycemic variability is associated with a lower survival rate in breast cancer patients

  • [Monzavi-Karbassi 2016] Pre-diagnosis blood glucose and prognosis in women with breast cancer

Accelerated aging:

  • [Chia 2018] Age-related Changes in Glucose Metabolism, Hyperglycemia, and Cardiovascular Risk

On exercise’s effect

Regulation of glucose metabolism by exercise: 

  • [Evans 2019] Regulation of Skeletal Muscle Glucose Transport and Glucose Metabolism by Exercise Training

The importance of increased muscle mass in homeostasis:

  • [Yang 2014] Enhanced Skeletal Muscle for Effective Glucose Homeostasis

The effect of exercise after a meal, although this is for type 2 diabetes but the same implications apply for healthy individuals

On the development of type 2 diabetes

Here’s a couple of findings from research at Yale University about how type-2 diabetes develops:

On defining glycemic variability and what it is associated with

In short, glycemic variability can be calculated in many methods (Standard deviation, MAGE, CONGA etc.) its goal is to have a measure that represents how glucose varies during the day.

It has been used to predict outcomes in people with diabetes and to assess glycemic control, but it has many associations in healthy individuals as well

In this figure, you can see three hypothetical patients A, B, C with the same mean but different patterns.

Why the trend in glucose is a better indicator than the mean alone:

  • [Ceriello 2008] Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients

The above is even more important when glucose is being measured through a CGM.

On interstitial glucose specifically

Interstitial glucose is what the Freestyle Libre measures, it is a bit different from blood glucose in a non-steady state, but the patterns are usually similar: