What the research found
Researchers examined continuous glucose monitor (CGM) data from children and adolescents with type 1 diabetes, focusing on brief spikes in blood glucose that lasted between 10 and 60 minutes and were surrounded by normal readings. They found these isolated hyperglycaemic episodes were surprisingly common—accounting for nearly one in five high-glucose events recorded. On average, participants experienced around 19 of these episodes per day during daytime hours.
When the researchers mathematically removed these brief spikes from the CGM data and recalculated standard metrics, the results shifted modestly but measurably. Time in range (TIR)—the percentage of time glucose stays within target—improved by roughly 1.7 percentage points, and average glucose dipped by 0.18 mmol/L. However, the effect was most pronounced around mealtimes, where removing these transient events revealed glucose differences of up to 1.3 mmol/L. The key insight: two patients with nearly identical overall CGM reports could have very different patterns of these brief spikes.
Why it matters for you
If you're using CGM data to guide insulin dosing or protocol adjustments, this matters because your standard metrics—the TIR and median glucose your clinician reviews—are partially inflated by events that may not reflect genuine metabolic control. A patient optimising for better metrics might intensify insulin dosing to eliminate these brief excursions, when the actual issue could be transient (perhaps post-meal dynamics or sensor noise) rather than requiring systemic change.
For tracking purposes on MyKine, this highlights why snapshot metrics alone can obscure important patterns. If you're logging glucose data alongside meals, training, or sleep timing, correlating where these brief spikes cluster matters more than their contribution to whole-day averages. Someone seeing frequent isolated post-meal spikes might benefit from protein timing or meal composition tweaks rather than insulin increases.
The research also suggests that if you're comparing your CGM metrics week-to-week, don't assume a small TIR improvement represents meaningful change—some of it may simply reflect fewer incidental spikes rather than sustained better control.
Caveats
- Retrospective, single-centre data; findings in paediatric T1D patients may not generalise to adults or other metabolic conditions
- Approach relies on modelling and replacing brief spikes with "plausible trajectories"—not measuring actual underlying physiology or cause
- No data on whether these spikes pose genuine health risk or are clinically meaningful; definition of 10–60 minute episodes is somewhat arbitrary
- CGM accuracy and sensor lag can themselves create or obscure brief signal spikes, potentially inflating prevalence estimates