持続血糖モニタリング：非糖尿病判定でも血糖変動症例存在

そのうち、CGM(持続血糖モニタリング)によるpre-diabetes、糖尿病診断・・・となるのだろうか？

随時血糖、空腹時血糖、HbA1ｃによる糖尿病診断というのは確かにCGMから見れば荒っぽい

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CGMを用いた57名の糖尿病・非糖尿病比検査に、3つの"glucotype"を同定

標準血糖測定では正常だったが、CGMで高値血糖変動ある症例あり、pre-diabetesレベルで15％、糖尿病レベル2％存在



Glucotypes reveal new patterns of glucose dysregulation
Published: July 24, 2018https://doi.org/10.1371/journal.pbio.2005143
http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005143






Classification of CGM with classes of glycemic signatures.

(A-C) Segregation of the 2.5-hour windows into the three classes of glycemic signatures derived from spectral clustering. The lines in each panel show an example of the glycemic signatures in each class. This separation of windows explains approximately 73% of the variance.
(D) One day of CGM data for 3 separate individuals. Color indicates classification of glycemic signatures. Note that since overlapping windows were used for clustering and classification, some periods of the day have multiple classifications.
(E) Heat map showing the fraction of time individuals spent in each of the glycemic classes.

Correlation between glycemic signature classes and measures of glucose homeostasis.

(A) Forest plots for each of the glucotypes. A Pearson’s correlation test was used to determine the correlation between the clinical metabolic tests—listed 1 per line—and the fraction of time spent in each glucotype class (S5 Data). The center dot line is the resulting correlation coefficient, with the line representing the corresponding 95% confidence interval.
(B) Forest plot with the lines representing age and BMI.
 (C) OGTT 2hr is plotted against the fraction of time in the severe glucotype for each individual. The line of best fit is shown in blue with the 95% confidence interval shaded in gray.