  Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have
disastrous long-term health consequences. An essential component of clinical
interventions for T2DM is monitoring dietary intake to keep plasma glucose
levels within an acceptable range. Yet, current techniques to monitor food
intake are time intensive and error prone. To address this issue, we are
developing techniques to automatically monitor food intake and the composition
of those foods using continuous glucose monitors (CGMs). This article presents
the results of a clinical study in which participants consumed nine
standardized meals with known macronutrients amounts (carbohydrate, protein,
and fat) while wearing a CGM. We built a multitask neural network to estimate
the macronutrient composition from the CGM signal, and compared it against a
baseline linear regression. The best prediction result comes from our proposed
neural network, trained with subject-dependent data, as measured by root mean
squared relative error and correlation coefficient. These findings suggest that
it is possible to estimate macronutrient composition from CGM signals, opening
the possibility to develop automatic techniques to track food intake.
