Through continuous monitoring

Abstract

The identification of the presymptomatic stages of type 1 diabetes (T1D) and the availability of disease-modifying therapies have redefined the goals of metabolic monitoring. The aim is no longer merely to establish a diagnosis, but rather to characterize biological progression, stratify risk, and identify the optimal time for intervention. This paradigm shift requires us to reconsider whether current tools and biomarkers adequately address these emerging needs.

In this context, continuous glucose monitoring (CGM) offers particularly attractive features. Unlike the oral glucose tolerance test (OGTT), CGM enables characterization of glucose homeostasis under real-life conditions by integrating glycemic variability and the temporal evolution of the glucose profile.

In the presymptomatic stages, CGM assessment is primarily based on conventional metrics. A higher percentage of time above 140 mg/dL (TA140) and an increased coefficient of variation (CV) have been associated with a greater risk of progression to clinical diabetes. However, their validation has mainly focused on demonstrating concordance with the OGTT or with thresholds derived from it. These findings support the value of CGM for identifying individuals at increased risk, but also highlight the need to develop CGM-derived biomarkers that reflect the biology of these stages and enable the definition of clinically relevant thresholds to intensify monitoring, evaluate disease-modifying therapies, or promptly identify the transition to stage 3.

The real challenge is not to demonstrate that CGM can reproduce OGTT results or to search for a single biomarker equivalent to the 120-minute glucose value, but rather to understand which aspects of glucose dynamics reflect the biological evolution of the disease. The potential of CGM lies in its ability to integrate multiple dimensions of the glucose signal—magnitude, variability, temporal organization, and longitudinal evolution—to identify dynamic, reproducible, and clinically meaningful metrics that capture metabolic progression under real-life conditions. The development of specific biomarkers and CGM-based predictive models could contribute to a more physiological and accurate staging of T1D, consistent with the paradigm of early intervention.