“Significantly higher glycemic variability in SD [standard deviation] and CV [coefficient variability] was observed for the underweight participants in comparison with the normal, which can be partly attributed to irregularity of eating habit (eating habit between meals) in free-living conditions,” according to investigators of a recent study published in PLOS ONE.
Investigators conducted a cross-sectional study, which was part of a broader research project titled “Exploring the impact of nutrition advice on blood sugar and psychological status using continuous glucose monitoring (CGM) and wearable devices.”
The study defined prediabetes based on HbA1c levels of 5.7% to 6.4% and/or fasting glucose levels of 100 mg/dL to 125 mg/dL. The research encompassed 40 participants who underwent 14 days of CGM and daily step tracking using FreeStyle Libre and Fitbit Inspire 2 devices. In addition to these physiological measures, dietary intake and eating behavior were assessed through self-administered dietary history questionnaires and modified questionnaires derived from Obesity Guidelines.
Participants in the prediabetes group exhibited higher levels of glycemic variability compared with the healthy group, with the most significant difference observed in the mean amplitude of glycemic excursions (MAGE).
In multivariate analysis, the presence of prediabetes emerged as a significant predictor of higher-than-median MAGE levels. Furthermore, the underweight group (body mass index [BMI] <18.5) displayed significantly higher SD and CV in glucose levels compared with the normal group, potentially attributed to irregular eating habits. On the other hand, the overweight group (BMI ≥25) spent the longest time above the 140 mg/dL or 180 mg/dL glucose range, which was linked to their eating patterns and reduced physical activity.
This study underscores the value of combining CGM with diet and activity tracking to optimize blood sugar control, especially in individuals with non-normal BMI.
The findings suggest that those with lower BMI, or underweight individuals, may benefit from improved dietary regularity, while those with higher BMI, or overweight individuals, may require tailored interventions to address their eating habits and physical activity levels. By leveraging the insights gained through concurrent CGM with diet and activity monitoring, health care providers can develop more personalized strategies for glycemic control, even in healthy nondiabetic adults.
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