Achieving optimal metabolic health and glycemic control is a common goal among not only diabetics, but also for healthy individuals, athletes, elite military operators and for infectious disease prevention and resilience. No isolated biomarker is currently ubiquitously accepted as a marker of overall metabolic health and most rely on isolated snapshot (single time point) analyses and not a continuous closed-loop biomarker data assessment. Glycosylated hemoglobin (A1c) provides limited characterization of glycemic variability, which contributes to the progression of glycemic dysregulation. For example, emerging evidence links the amplitude and duration of glycemic variability as an independent risk factor linked to cardiovascular disease (CVD) (Di Flaviani 2011, Monnier 2006). Hyperglycemia-induced endothelial dysfunction and oxidative stress are greater with larger glycemic variability (Monnier 2006, Buscemi 2010). Glycemic variability is more deleterious for the cardiovascular system than sustained hyperglycemia (Nalysnyk 2010). Few technologies allow for continuous biomarker monitoring over time, and under a range of conditions like daily activities, swimming, exercise, sleep, etc. Multiple lines of evidence strongly suggest the predictive impact and value of monitoring glycemic variability on acute and chronic health of diabetes populations and non-diabetes populations (Rodriguez-Segade 2018, Zeevi 2015). Thus, there has been emerging interest in therapeutic approaches that seek to reduce glycemic variability. This potential for early detection of glycemic dysregulation is likely to be the single most beneficial effect of using CGM as an informational device, especially in the context of other biomarkers measures periodically. It is likely that people will make lifestyle modifications if they are aware of an impending health problem, detected through real time GCM-tracked glycemic variability. Lifestyle modifications are proven to be the most effective intervention for restoring normal fasting glucose levels and preventing diabetes among dysglycemic subjects, reducing the conversion to diabetes by 58% over placebo, and by 39% over metformin in one large US study (Diabetes Prevention Program Research Group 2002). Long terms follow-ups on other international studies have shown equally significant results at 4 years (Tuomilehto 2001) and 14 years (Li 2008) after the controlled lifestyle interventions ended, including reductions in diabetes incidence of 58%, and 43% respectively.
It is known that metabolic health is on a spectrum and long-term studies in diabetic populations have demonstrated that reducing glycemic variability is more important than lowering baseline hyperglycemia in terms of reducing cardiovascular complications (Hall 2018). Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health in non-diabetics since the potential benefits of metabolic awareness extend beyond the diabetic population. Emerging technology that can provide tight feedback on lifestyle effects could be a valuable mechanism for non-diabetics seeking to improve education and reduce their lifetime risk of disease. Though such outcomes have not yet been demonstrated in long term studies, the existing research reveals promising results, including improved screening for metabolic risk (Rodriguez-Segade 2018), clear observability of effects of lifestyle intervention (Hall 2018, Brynes 2005, Freckmann 2007), and acceptance of a minimal-risk strategy for use as a preventative tool in a non-diabetic population (Liao 2018). The Diabetes Prevention Program Research Group called for a shift in response in order to reverse these trends, stating that: "methods of treating diabetes remain inadequate and that prevention is preferable (Diabetes Prevention Program Research Group 2002)." Though unproven as a preventative measure, monitoring of glycemic variability is - at worst - unlikely to exacerbate the problem. At best, however, if it becomes a widespread lifestyle tool, the benefits of improved individual metabolic awareness and educated action could have compounding effects at a larger societal scale.
Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health with improved glycemic monitoring technologies (Danne 2017). It is becoming clear, that in addition to diabetic populations, normal, healthy populations can benefit from stable, controlled blood sugar levels, and that feedback mechanisms, including wearable technologies, can be employed. Thus, CGM could be a promising method of improving biomarkers of metabolic health for virtually anyone. In addition, optimal metabolic health is typically associated with improved behavioral health and cognitive resilience and decision making (Hadj-Abo 2020). Thus, optimizing and monitoring glycemic control may be useful for mental health and may be a valuable tool for military personnel and first responders under metabolic stress. Advances in software and hardware technologies have been developed to measure, analyze and predict glycemic variability and provides insight on how this dynamic biomarker correlates to metabolic fitness. Specifically, new advances in CGM technologies offer the potential to monitor, predict and change behavior through a closed-loop feedback system. By comparing CGM data with blood markers of metabolic health (eg.HbA1c , Insulin, etc.), and inflammation (e.g. hsCRP, cytokines) and along with assessments of emotion, cognition and behavior, a more robust interpretation and deconvolution of CGM data with experimental interventions may be possible.
