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Updated: May 8, 2026

A Zebrafish Model of Diabetes Mellitus and Metabolic Memory

A Zebrafish Model of Diabetes Mellitus and Metabolic Memory

Published on: February 28, 2013

Continuous Glucose Monitoring-Derived Metrics and Cardiovascular Risk Among People With Diabetes: Systematic Scoping

Helene Bei Thomsen^1,2, Benjamin Lebiecka-Johansen¹, Ole Nørgaard³

¹Steno Diabetes Center Aarhus, Palle Juul-Jensens Boulevard 11, Aarhus, Central Denmark Region, 8200, Denmark, +45 23 70 74 81.

|May 6, 2026

Summary

Continuous glucose monitoring (CGM) data show inconsistent associations with cardiovascular risk in diabetes. More longitudinal studies are needed to explore the prognostic value of CGM metrics for cardiovascular disease outcomes.

Keywords:

association blood glucose cardiovascular disease cardiovascular risk continuous glucose monitoring diabetes prediction scoping review

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Last Updated: May 8, 2026

A Zebrafish Model of Diabetes Mellitus and Metabolic Memory

A Zebrafish Model of Diabetes Mellitus and Metabolic Memory

Published on: February 28, 2013

Area of Science:

Cardiology
Endocrinology
Data Science

Background:

Conventional clinical markers are used for cardiovascular risk stratification.
Continuous glucose monitoring (CGM) data are currently underutilized in cardiovascular risk prediction models.
A literature synthesis is needed to understand the prognostic relevance of CGM data for cardiovascular outcomes in individuals with diabetes.

Purpose of the Study:

To identify studies examining the association between glycemic control and cardiovascular outcomes.
To evaluate the predictive value of CGM-derived metrics in cardiovascular risk assessment.

Main Methods:

A comprehensive literature search of MEDLINE and Embase was conducted up to March 11, 2025.
Peer-reviewed, original research including CGM-derived metrics and cardiovascular disease (CVD) outcomes were included.
Two independent reviewers screened the identified records.

Main Results:

53 studies were included, focusing on various diabetes types and prediabetes.
A wide range of CGM metrics and CVD outcomes (clinical and subclinical) were investigated.
Findings were inconsistent, with methodological limitations noted; Time-in-range was most studied, while mean amplitude of glycemic excursions showed consistent association with CVD.

Conclusions:

The prognostic value of CGM-derived metrics for CVD outcomes remains underexplored.
Methodological weaknesses, such as underpowered analyses, contributed to inconsistent findings.
Longitudinal prediction studies utilizing routinely collected CGM data for clinical CVD outcomes are essential.