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Updated: Jun 16, 2026

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Published on: June 11, 2012
Bridging the Continuous Glucose Monitoring decision gap: from glycaemic variability data to actionable stability in
Qingmei Wang1, Fang Pan2, Bowu Li3
1Department of Nursing, Beijing Hepingli Hospital, Beijing, China.
None:
The 2026 proposal to stage type 2 diabetes by β-cell trajectory and continuous glucose monitoring (CGM) metrics-specifically time in tight range (TITR)-represents a pivotal advance in dysglycaemia phenotyping. Yet, this innovation exposes a critical translational void: actionable algorithms to convert granular CGM outputs into stage-specific, stability-oriented therapeutic responses remain absent from clinical guidelines. This Perspective identifies three barriers perpetuating this decision gap: (i) metric overload in ambulatory glucose profiles, unaccompanied by decision-support pathways linking variability and TITR decline to therapeutic escalation; (ii) clinical inertia that prioritizes hypoglycaemia avoidance (TBR) over stability optimization (TITR, CV reduction), despite compelling evidence associating glycaemic variability with cardiovascular sequelae; and (iii) a conspicuous absence of structured frameworks for embedding CGM-derived behavioural insights into sustainable lifestyle routines. To address these impediments, a pragmatic, three-step closed-loop clinical model is proposed, stratifying CGM interpretation and action thresholds according to the new disease stages (1-3b). Central to this framework is the prioritization of sustainable glycaemic stability-operationally defined by a coefficient of variation ≤36%, stage-appropriate TITR attainment, and time below range <4%. The model offers provisional templates for stage-concordant lifestyle prescription and pharmacotherapy coordination, while acknowledging the risk of overtreatment, the importance of patient-reported outcomes, and the substantial implementation barriers that constrain real-world adoption. Bridging the CGM decision gap demands prospective validation of stage-specific targets and the seamless integration of context-aware decision-support tools into electronic health records.
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