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<p>Continuous glucose monitors (CGMs) provide detailed glucose profiles, but their relevance to health outcomes in individuals without diabetes remains unclear. Here we assess time in range (TIR<sub>3.9–5.6</sub> and TITR<sub>3.9-7.8</sub>) and glycaemic variability in individuals (N = 3,634; age 46 ± 12 y; 83% female; BMI 27 ± 6 kg/m²) from PREDICT 1 (NCT03479866), PREDICT 2 (NCT03983733), and PREDICT 3 (NCT04735835) without diabetes or prediabetes, and explore associations with demographic, diet, lifestyle, cardiometabolic markers, and predicted cardiovascular risk. Outcomes are non-pre-defined exploratory analyses. Higher TIR<sub>3.9–5.6</sub> is associated with lower HbA1c, OGTT glucose, carbohydrate intake, and higher protein intake. Sleep duration is inversely correlated with mean glucose. TIR<sub>3.9–5.6</sub> provided moderate discrimination for predicted ASCVD 10-year risk (AUC = 0.75). While CGM metrics show potential to capture some components of glycaemic physiology, longer-term health outcomes are required to demonstrate whether CGM monitoring has utility for health management in euglycaemic individuals.</p>

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Associations of continuous glucose monitor derived time in range and glycaemic variability with diet lifestyle and demographics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Kate M. Bermingham,
  • Harry A. Smith,
  • Emma L. Duncan,
  • Javier T. Gonzalez,
  • Ana M. Valdes,
  • Paul W. Franks,
  • Linda Delahanty,
  • Hassan S. Dashti,
  • Richard Davies,
  • George Hadjigeorgiou,
  • Jonathan Wolf,
  • Andrew T. Chan,
  • Tim D. Spector,
  • Sarah E. Berry

摘要

Continuous glucose monitors (CGMs) provide detailed glucose profiles, but their relevance to health outcomes in individuals without diabetes remains unclear. Here we assess time in range (TIR3.9–5.6 and TITR3.9-7.8) and glycaemic variability in individuals (N = 3,634; age 46 ± 12 y; 83% female; BMI 27 ± 6 kg/m²) from PREDICT 1 (NCT03479866), PREDICT 2 (NCT03983733), and PREDICT 3 (NCT04735835) without diabetes or prediabetes, and explore associations with demographic, diet, lifestyle, cardiometabolic markers, and predicted cardiovascular risk. Outcomes are non-pre-defined exploratory analyses. Higher TIR3.9–5.6 is associated with lower HbA1c, OGTT glucose, carbohydrate intake, and higher protein intake. Sleep duration is inversely correlated with mean glucose. TIR3.9–5.6 provided moderate discrimination for predicted ASCVD 10-year risk (AUC = 0.75). While CGM metrics show potential to capture some components of glycaemic physiology, longer-term health outcomes are required to demonstrate whether CGM monitoring has utility for health management in euglycaemic individuals.