New research shows that CGM data aids weight loss
A small randomized clinical trial measures the value of seeing real-time glucose to improve diet and body composition
The Study
Personalized nutrition therapy without weight loss counseling produces weight loss in individuals with prediabetes who are overweight/obese: a randomized controlled trial
Where: George Mason University
When: Published July 2024 in the journal Nutrients
The Takeaway
This randomized clinical trial examined the effects of personalized nutrition therapy combined with continuous glucose monitoring (CGM) on body weight and composition in people with prediabetes who also have obesity or are overweight.
The research shows that people with these linked conditions can achieve fat loss and overall weight loss when provided education about glucose (blood sugar) control without an exclusive emphasis on a weight loss goal. The real-time CGM feedback about how different foods affect blood sugar enhances these results.
What it Looked At
The researchers randomly assigned 30 participants with prediabetes who also had obesity or were overweight to two treatment arms and followed up with them for 30 days. Each treatment arm included 15 participants, 11 female and 4 male.
Both groups were given recommendations for calorie intake and macronutrient distribution. Researchers paired these recommendations with personalized goal setting for glucose control and healthy eating but did not specifically emphasize weight reduction or changes in physical activity.
The 15 people considered the treatment arm of the study received personalized nutrition therapy and CGM. These participants could access their CGM data on their mobile devices in real time. The other 15 participants—the control group—also wore a CGM but couldn’t access their glucose data. Every 10 days, the researchers replaced CGM sensors and took various weight and body composition measurements.
For the first 10 days of the trial, researchers asked both groups to follow their usual diet and engage in their regular physical activity routines. After those 10 days, researchers provided the participants personalized nutrition therapy from a dietitian.
The personalized nutrition therapy was tailored to each person’s energy needs for maintaining their current weight based on their sex, weight, height, age, and activity level. (To calculate energy needs, researchers used the Mifflin-St Jeor equation, which is used to determine resting metabolic weight.) They prescribed participants a moderate carbohydrate diet with a macronutrient breakdown of 50% carbohydrates, 20% protein, and 30% fat. They also taught participants about food groups, serving sizes, and macronutrient composition. Plus, they offered dietary recommendations based on personal food preferences. Neither group got recommendations on changing their physical activity levels.
For participants in the treatment arm, the dietitian reviewed the daily CGM data tied to their food choices every 10 days. The dietitian flagged any foods that elevated a participant’s blood glucose above 140 mg/dL and recommended that they minimize or eliminate these foods.
The control group also received nutrition education consultations from the dietitian. However, neither the participants nor the dietitian had access to the participants’ CGM data, so nutrition recommendations were not based on their glucose readings.
What it Found
The treatment group experienced a two-fold greater reduction in both their weight and fat mass compared to the control group. They also showed a greater average reduction in BMI. The treatment group significantly decreased their carbohydrate intake and boosted their physical activity time. And they showed greater adherence to dietary recommendations.
Both groups lost weight during the study. However, the average weight loss in the treatment group was about 4 pounds, compared to about 2 pounds in the control group. At the start of the study, participants’ average fat mass was slightly higher in the treatment group compared to the control group. However, the treatment group experienced a greater reduction in fat mass (2.4 pounds) compared to the control group (1.1 pounds). Both groups also experienced a reduction in BMI. However, the treatment group showed a greater decrease (0.72 kg/m2) compared to the control group (0.38 kg/m2).
At the study’s start, there were no significant differences in the physical activity levels between groups. However, in the treatment group, physical activity minutes increased from 390 to 496 (about 27%). In the control group, the time increased from 480 to 489 (about 2%).
The researchers only noted a slight difference in carbohydrate intake between the two groups. Average carb consumption decreased about 6 percentage points in the treatment group and about 2 percentage points in the control group.
Finally, while both groups improved their adherence to dietary recommendations, the treatment group improved more than the control.
The researchers note that, overall, personalized nutrition therapy is effective for weight and fat loss in people with prediabetes who also have obesity or are overweight. However, they add, “Enabling participants to observe the immediate effects of dietary changes on their blood glucose levels enhanced compliance and the intervention’s effectiveness. Furthermore, utilizing CGMs as a behavior modification tool minimized unnecessary dietary restrictions.” Those with access to CGM data also had more dietary flexibility, which the researchers say could be another reason for the increased compliance in the treatment group.
Why it Matters
To their knowledge, the researchers say that their study is the first to evaluate personalized nutrition therapy in conjunction with CGM as an intervention to improve weight and body composition in people with these conditions.
The study authors point to several previous studies linking obesity and metabolic dysfunction. Increased fat mass is associated with worsening cardiometabolic risk factors, such as blood pressure, lipids, and insulin resistance, for example. Additionally, a higher body fat percentage is linked to less time spent in range (of healthy blood sugar levels) on CGM data for people with Type 2 diabetes. In people with prediabetes, higher fat levels correlate with higher glucose levels and hemoglobin A1C (HbA1c), a measure of average glucose levels over the last three months. Therefore, improving body composition by reducing fat mass may improve metabolic health.
For people 45 and older who have prediabetes, the 10-year risk of progressing to Type 2 diabetes ranges from about 10% to nearly 25%, according to a prospective population-based Rotterdam Study in the Netherlands. However, the lifetime risk of progressing ranges from about 50% to 80%.
Yet, even with this risk, research shows that prediabetes is vastly underdiagnosed in the United States and often goes untreated. About 80% to 90% of people with prediabetes don’t know they have it. One study found that out of more than 13,000 patients screened correctly and eligible for a prediabetes diagnosis, none received treatment in line with U.S. Preventive Services Task Force’s (USPSTF) guidelines.
Type 2 diabetes increases one’s risk for significant health issues and early death. Having prediabetes also increases the risk of stroke, heart disease, and Alzheimer’s. And studies find prediabetes is associated with higher healthcare costs.
CGM is a tool most used by people with diabetes. However, this study demonstrates that access to real-time blood sugar data can help people with prediabetes improve their metabolic health and that guidance on using that data can improve weight and body composition.
