ArticleDiabetes care2026
The Association Between Ambient Temperature and Hypoglycemia in People Living With Type 1 Diabetes: A Case Time Series Analysis Using Real-Time Continuous Glucose Monitoring.
Article in Diabetes care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveWe investigated the short-term association between ambient temperature and risk of hypoglycemia in adults with type 1 diabetes mellitus (T1DM). We hypothesized that higher ambient temperature would increase the odds of hypoglycemia developing. RESEARCH DESIGN AND
methodsWe applied a case time series design to assess the longitudinal association between ambient temperature and hypoglycemia measured using routine continuous glucose monitoring data from individuals with T1DM. A quasi-binomial fixed-effect regression with distributed lag nonlinear models was used to estimate potentially nonlinear and lagged risks of nonoptimal temperature on hypoglycemic episodes, defined as ≥15 min of glucose concentration <3.9 mmol/L. The model was adjusted for long-term trends, seasonality, day of the week, and public holidays. A secondary outcome was change in daily mean glucose concentration.
resultsWe analyzed 32,966,282 glucose readings from 679 adults with T1DM attending two National Health Service clinics in Sussex, England, between 2017 and 2024. Higher ambient temperatures were associated with an increased risk of hypoglycemia. The risk increased nonlinearly for temperatures above 13°C, with the odds ratio reaching 1.26 (95% CI 1.13-1.26) at 25°C. The strongest effect was observed on the same day of the exposure, and it diminished over subsequent days. In the secondary analysis, higher temperatures were associated with lower mean glucose levels.
conclusionsElevated ambient temperature significantly increases the short-term risk of hypoglycemia in adults with T1DM. These findings are specific to the U.K. population and climate, which may limit generalizability. Our results support anticipatory insulin adjustments during hot weather and consideration of ambient temperature in hybrid closed-loop insulin algorithms.
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