BACKGROUND: There is no consensus as to the best method to assess glycemic variability from continuous glucose monitoring (CGM) data. Rate of change has been suggested as a preferred method of assessing glycemic variability, but this assertion has not been validated. METHODS: Forty-eight hours of CGM data were analyzed from 22 subjects (seven controls and 15 with type 1 diabetes) purposively sampled to reflect a range of glycemic variability. SD, mean amplitude of glycemic excursion, continuous overall net glycemic action, SD of rate of change (SDRC), and average absolute rate of change (AARC) were calculated and correlated with a clinical assessment of variability. SDRC and AARC were recalculated following a data smoothing process involving aggregation. RESULTS: SDRC calculated from non-aggregated glucose readings gives a weaker correlation (r = 0.66) with the clinical assessment of variability than the correlations obtained by other indices (r = 0.90-0.96). Following a process of data aggregation, to exclude clinically insignificant fluctuations of blood glucose, we demonstrated that 60 min was the optimal aggregation period. The correlation between clinical assessment of variability and SDRC, 60-min aggregated, is 0.93, which is comparable to correlations shown by other established indices. Similar results are obtained for AARC. CONCLUSIONS: Rate of change calculated after appropriate data aggregation is a valid index of glycemic variability. Optimal data aggregation is achieved by aggregating into 1-h blocks.
BACKGROUND: There is no consensus as to the best method to assess glycemic variability from continuous glucose monitoring (CGM) data. Rate of change has been suggested as a preferred method of assessing glycemic variability, but this assertion has not been validated. METHODS: Forty-eight hours of CGM data were analyzed from 22 subjects (seven controls and 15 with type 1 diabetes) purposively sampled to reflect a range of glycemic variability. SD, mean amplitude of glycemic excursion, continuous overall net glycemic action, SD of rate of change (SDRC), and average absolute rate of change (AARC) were calculated and correlated with a clinical assessment of variability. SDRC and AARC were recalculated following a data smoothing process involving aggregation. RESULTS: SDRC calculated from non-aggregated glucose readings gives a weaker correlation (r = 0.66) with the clinical assessment of variability than the correlations obtained by other indices (r = 0.90-0.96). Following a process of data aggregation, to exclude clinically insignificant fluctuations of blood glucose, we demonstrated that 60 min was the optimal aggregation period. The correlation between clinical assessment of variability and SDRC, 60-min aggregated, is 0.93, which is comparable to correlations shown by other established indices. Similar results are obtained for AARC. CONCLUSIONS: Rate of change calculated after appropriate data aggregation is a valid index of glycemic variability. Optimal data aggregation is achieved by aggregating into 1-h blocks.
Authors: Thomas A Peyser; Andrew K Balo; Bruce A Buckingham; Irl B Hirsch; Arturo Garcia Journal: Diabetes Technol Ther Date: 2017-12-11 Impact factor: 6.118
Authors: Michael R Rickels; Peter G Stock; Eelco J P de Koning; Lorenzo Piemonti; Johann Pratschke; Rodolfo Alejandro; Melena D Bellin; Thierry Berney; Pratik Choudhary; Paul R Johnson; Raja Kandaswamy; Thomas W H Kay; Bart Keymeulen; Yogish C Kudva; Esther Latres; Robert M Langer; Roger Lehmann; Barbara Ludwig; James F Markmann; Marjana Marinac; Jon S Odorico; François Pattou; Peter A Senior; James A M Shaw; Marie-Christine Vantyghem; Steven White Journal: Transplantation Date: 2018-09 Impact factor: 4.939
Authors: Michael R Rickels; Peter G Stock; Eelco J P de Koning; Lorenzo Piemonti; Johann Pratschke; Rodolfo Alejandro; Melena D Bellin; Thierry Berney; Pratik Choudhary; Paul R Johnson; Raja Kandaswamy; Thomas W H Kay; Bart Keymeulen; Yogish C Kudva; Esther Latres; Robert M Langer; Roger Lehmann; Barbara Ludwig; James F Markmann; Marjana Marinac; Jon S Odorico; François Pattou; Peter A Senior; James A M Shaw; Marie-Christine Vantyghem; Steven White Journal: Transpl Int Date: 2018-04 Impact factor: 3.782
Authors: Federico Bertuzzi; Luciano De Carlis; Mario Marazzi; Antonio Gaetano Rampoldi; Matteo Bonomo; Barbara Antonioli; Marta Cecilia Tosca; Marta Galuzzi; Andrea Lauterio; Danila Fava; Patrizia Dorighet; Andrea De Gasperi; Giacomo Colussi Journal: Cell Transplant Date: 2018-06-05 Impact factor: 4.064