Type 1 Diabetes Mellitus
Conditions
Keywords
Insulin sensitivity, Menstrual cycle, Diabetes management
Brief summary
This study evaluates the impact of menstrual cycle phases on insulin sensitivity, glycaemic control, and insulin requirements in women with type 1 diabetes (T1D). It aims to quantify differences between the follicular and luteal phases using data from the Tidepool Data Platform. Key endpoints include modelled insulin sensitivity, mean glucose, Time in Range (TIR), and total insulin dose. Secondary objectives include assessing phase-specific variability in the endpoints and differences between participants. The study follows a decentralized, anonymized data collection approach, with participants using continuous glucose monitors (CGMs) and insulin pumps or smart pens. Glycaemic outcomes and insulin requirements will be compared across menstrual cycle phases using linear mixed-effects models to account for repeated measures within individuals. Cycle phase-specific variation in insulin sensitivity will be quantified using a hierarchical Bayesian state-space model, which estimates a latent insulin sensitivity parameter from CGM time series data while accounting for measurement noise and temporal dynamics. The findings are expected to improve understanding of glycaemic dynamics across the menstrual cycle and inform future diabetes management strategies.
Interventions
None listed
Sponsors
DCB Research AG
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
People are eligible to participate in the study if they: * are living with T1D; * are willing to upload diabetes-related data to the Tidepool Data Platform; * are using a continuous glucose monitoring (CGM) system; * are using an insulin pump or a smart pen for most of their insulin delivery; * are willing to track and share their menstrual cycle data; * are 18 years old or older; * give consent to share their anonymized data for research.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Insulin sensitivity | Between 1 and 12 months | Insulin sensitivity quantified by using a hierarchical Bayesian state-space model that estimates a latent insulin sensitivity parameter from CGM time series data. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Mean glucose | Between 1 and 12 months | Mean daily glucose based on CGM data |
| Time in range (TIR) | Between 1 and 12 months | Mean daily TIR based on CGM data |
| Time above range (TAR) | Between 1 and 12 months | Mean daily TAR based on CGM data |
| Time belwo range (TBR) | Between 1 and 12 months | Mean daily TBR based on CGM data |
| Total daily insulin | Between 1 and 12 months | Mean daily total insulin based on insulin pump data |
| Total basal insulin | Between 1 and 12 months | Mean daily total basal insulin based on insulin pump data |
| Total bolus insulin | Between 1 and 12 months | Mean daily total bolus insulin based on insulin pump data |
Countries
Switzerland
Contacts
PRINCIPAL_INVESTIGATORMartina Rothenbühler, PhD
DCB Research AG
Outcome results
None listed