Abstract
Introduction: Continuous glucose monitoring (CGM) devices record blood glucose every few minutes over a maximum 2-week wear period. Despite the richness of these data, existing first and second moment statistics (such as mean glucose) fail to capture the "shape" of variation in the CGM time-series.
Objective: We developed novel, reproducible CGM shape metrics using functionals of the periodogram, a decomposition of CGM signal (Figure 1A) variance, to comprehensively characterize glucose patterns.
Methods: We analyzed CGM data from N = 160 HYPNOS clinical trial participants with type 2 diabetes who wore CGM sensors twice, 3 months apart. We first calculated log-transformed periodograms for each person-period. To these decompositions, we fit piece-wise linear models over 3 frequency ranges: less than 1/24 hrs-1 (long term patterns), 1/24 to 2/5 hrs-1 (daily diet patterns), and greater than 2/5 hrs-1 (immediate fluctuations due to food). Slopes of fit segments, value at first frequency, and values at segment midpoints were extracted as our initial metrics (Figure 1B). We estimated and compared the within-person test-retest correlation for our shape measures against existing CGM and glucose metrics: mean glucose, time-in-range, and HhbA1c.
Results: The highest raw correlation among the new shape metrics (r = 0.737) was comparable to that of the existing metrics (r = 0.798) (Figure 1C). Even after adjusting the shape metrics for existing metrics, there was a maximum test-retest correlation of r = 0.778. The shape metrics also carried information distinct from existing metrics. According to mixed effect models fit to data from both wear periods, the shape metrics explain less than 16% of variance in any existing metric when controlled for the other existing metrics.
Conclusion: These new shape metrics carry reproducible information distinct from that of standard CGM metrics and HbA1c. The next step is to evaluate whether these novel shape metrics, which leverage the granularity of CGM data, can be linked to clinical endpoints.