Metabolic health has historically been evaluated using fasted blood glucose (BG), oral glucose tolerance, and glycated hemoglobin (HbA1c). However, these tests do not capture real-world BG dynamics and their relation to health and behavior. Controlled testing conditions like fasting and oral glucose challenges allow for standardization in screening for existing glucose dysfunction, but do not capture the earliest deviations from healthy glycemic patterns that an individual may exhibit prior to overt manifestation of disease.
Fasting BG is used to diagnose normal, pre-diabetic, and diabetic status. This test leaves the vast majority of glycemia unevaluated as it is a single time point, repeated at annual or longer intervals in people without diabetes1. As fasting BG elevation may not occur until overt metabolic dysfunction is established, the earliest window for intervention may be missed.
The Oral Glucose Tolerance Test (OGTT) measures the metabolic response to a standardized oral intake of glucose (up to ~100g)2,3. While this test captures the glucose dynamics that inform status/onset of insulin resistance, beta-cell dysfunction, etc., it has several limitations. The OGTT assumes that all individuals are adapted to a high-carbohydrate diet2. Low-carbohydrate diets reduce metabolic reliance on glucose and increase fat oxidation efficiency4, and even the lack of a single high-carbohydrate meal prior to the OGTT can negatively impact test results5. Thus, impaired glucose tolerance on low-carbohydrate diets may not reflect poor metabolic health, but physiologic adaptation4. Additionally, like fasted BG, the OGTT does not capture the real-world behaviors and metabolic responses. Together, the OGTT provides a great tool for establishing metabolic response to a specific challenge, but provides a limited window into an individual’s response to glycemic challenges experienced in real life and may be confounded by recent and long-term dietary choices.
HbA1c evaluates the end product of hemoglobin glycation, which is associated with serum BG exposure over the lifespan of red blood cells, about 3-4 months6. Although HbA1c is a gold standard for assessing diabetes risk, and for diabetes management, it is not always elevated in concert with fasting BG or the OGTT7. Additionally, factors including iron and vitamin b-12 levels, and genetic variants can confound the interpretation of an individual’s HbA1c8. This has led to the development of other tests for chronic glycemic dysregulation (e.g., fructosamine9), and demonstrates a need for more effective tools to evaluate glycemia at the level of the individual.
Continuous glucose monitors (CGM) provide 5-minute resolution, 24-hour blood glucose using an enzymatic reaction that reflects glucose in the interstitial fluid (intermediate location) of reach peripheral cellular tissues (end product and uptake location). CGM also captures 24-hour/day glycemic dynamics, including circadian rhythms and postprandial responses10,11, that are more reflective of metabolic status than a single data capture time point (Fasting BG) or window (OGTT). This enhanced resolution has been the reason for recent shifts toward using CGM-based dynamics as more accurate measures of glycemic control in diabetic populations12.
Recent improvements in CGM device functionality, form factor, and cost, have greatly increased adoption among people with diabetes13, with associated improvements in subjective stress14, glycemia12, and management burden15. The potential benefits of metabolic awareness extend beyond people with diabetes: technology that can provide closed-loop feedback on the impact of lifestyle choices on glucose could support individuals across the spectrum of metabolic health. Researchers have begun to characterize continuous glucose dynamics in healthy participants to serve as a benchmark from which to understand progression to disease. However, few studies (<20) have evaluated continuous glucose in heterogeneous populations, and none have observed large populations (n>10,000) to generate reference glycemic values, patterns, usage analysis in people without diabetes. Most have had small sample sizes (tens, or, very occasionally, hundreds)16–18or have used early-generation CGMs17,19,20 with lower accuracy21. Many of these evaluations also involve controlled laboratory tests and interventions, which further influence participant glycemic control.
CGM data in controlled settings does not reflect the rapidly emerging use case of individuals wearing CGMs in everyday life, and in concert with insight-driven software for personal metabolic awareness and lifestyle improvement. Personally-relevant education is a key component of the movement toward preventative-CGM use, predicated on the idea that a lack of real-time input available to the individual about the relationship between one’s lifestyle and metabolic function contributes to the epidemic of metabolic disease. A personal feedback mechanism like informational CGM could greatly increase awareness of the impacts of daily choices on glycemia.
However, glycemic patterns under these circumstances have never been studied at scale and may:
Generate important discoveries around patterns of glycemia in the general population.
Evaluate the utility of the CGM & associated software as a tool for general metabolic education & behavior change
Evaluate associations among use of the CGM & software and health outcomes.
We propose that these tools can link understanding of the impact of one’s food choices, physical activity, sleep, and stress on glycemia and, with guidance, encourage healthier choices22. Though outcomes for this population have not yet been demonstrated in long-term studies, short term studies report automated screening for metabolic risk in agreement with traditional tests23, detection of prediabetic/diabetic glucose levels in people without diabetes24, clear impacts of meal composition19,25,26–28, high user acceptability17, and minimal adverse effects19,22–24.
Over long periods, real-time glycemic-response feedback available from a guided CGM experience could inform and motivate positive behavior change around nutrition, sleep, and exercise. Several studies have used CGM data to observe significant improvements in postprandial glucose levels18–20 and glycemic variability29 with relatively simple adjustments in macronutrient composition and order of consumption18,19,25. One study used CGM data to determine optimal timing for bouts of exercise to manage postprandial glucose response in people at risk for developing diabetes28, while another showed a positive correlation between light activity and reduced glycemic variability among lower-fitness subjects12. A number of recent and ongoing studies have demonstrated evidence that CGM data can be used to predict personalized meal responses and the interpersonal variability present in glycemic patterns with standardized meals and behaviors18–20,30,31. These findings are echoed in online reports from non-diabetic CGM users, who have discovered large meal-dependent variations in postprandial glucose along with negative effects from sleep-deprivation and other stressors32,33.
The epidemic of diabetes was estimated to cost the US over $327 billion in 201716, with both rates of disease and cost of treatment climbing year after year.16,34 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, prevention is preferable.35”. Ten years later, CGMs demonstrably improve diabetes treatment and reversal, and show early promise for prevention. Though untested at scale as a preventative measure, a CGM combined with personalized guidance is a low-risk technology, ideal for use in wellness education and research proposed here. If CGMs become a widespread wellness tool, the benefits of improved individual metabolic awareness and informed lifestyle decisions could have compounding effects at a larger societal scale. Data-driven consumers opting for healthier food and drink options could shift the demand balance, influencing suppliers to modify their offerings and driving down the cost of healthier food items. Wider adoption could also contribute to medical research, as large anonymized and aggregated CGM datasets would provide unprecedented insight into full-spectrum glycemia, generate a clear definition of “healthy” glucose regulation, and improve understanding of the onset, progression, and treatment targets for metabolic disorders.
The proposed study and the follow-on research it could inform will be groundbreaking, in that it will be large in scale and the first to assess some or all of the following: