For the roughly 8 million people globally living with type 1 diabetes, continuous glucose monitoring has become indispensable — yet the very richness of the data it generates is now producing a scientific bottleneck that threatens the comparability of clinical trials and the reliability of treatment decisions. A comprehensive narrative review published in JMIR Diabetes maps this expanding and increasingly fragmented landscape, with implications for how glycemic success is defined, measured, and communicated across medicine.

The review systematically categorizes CGM-derived metrics into four tiers: standardized measures already embedded in clinical guidelines (such as time-in-range and glucose management indicator), clinical metrics suited to routine patient care, emerging metrics that capture more nuanced glycemic dynamics, and composite measures designed to integrate multiple dimensions of glycemic control into single scores. The core problem the authors identify is metric proliferation without harmonization — different devices, software platforms, and study protocols calculate ostensibly identical metrics through divergent algorithms, making cross-trial comparisons unreliable. Hemoglobin A1c, despite its known limitations in capturing glycemic variability, at least offered a universal yardstick; the CGM era has traded that uniformity for granularity without yet establishing equivalent consensus.

This review arrives at a critical inflection point. Regulatory agencies including the FDA have increasingly accepted CGM-based endpoints in pivotal diabetes trials, raising the stakes for metric standardization considerably. The lack of agreed-upon calculation standards for metrics like glucose variability indices or time-below-range thresholds is not merely academic — it has direct consequences for drug approvals, clinical guideline development, and real-world device adoption. As a narrative rather than systematic review, this work cannot resolve the standardization gap itself, but it performs a genuinely useful cartographic function, distinguishing metrics appropriate for clinical dashboards from those better confined to research contexts. That distinction alone may help clinicians avoid over-interpreting experimental metrics in daily practice.