Supplementary Data. Correlation analysis. Importance of normalizing indices before applying SPCA

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1 Supplementary Data Correlation analysis The correlation matrix R of the m = 25 GV indices calculated for each dataset is reported below (Tables S1 S3). R is an m m symmetric matrix, whose entries r ij = r ji represent the correlation among the i-th and j-th elements of the vector collecting all GV indices; the diagonal entries of the correlation matrix R are equal to 1, quantifying the correlation between a GV index and itself. Results obtained in the three cases are consistent with the correlation values documented in the literature Importance of normalizing indices before applying SPCA Normalizing GV indices before applying SPCA is key to avoiding the possibility that the heterogeneity of their scale (e.g.,,, and have numerical values larger than,, and Hypoglycemic Index) may influence the results. For instance, without normalization, results of SPCA would change when glucose concentration is measured in mmol/l (Table S4, right columns) rather than in mg/ dl (Table S4, left columns), simply because some indices (such as,,, and ) decrease their values by 18 times when converting from mg/l to mmol/l.

2 Supplementary Table S1. Dataset 1 (17 Continuous Glucose Monitoring Time-Series) w dm w dm M , Average Daily Risk Range;, Blood Glucose Risk Index;, percentage coefficient of variation;, Glycemic Risk Assessment Diabetes Equation;,, and, percentages of score due to euglycemia, hypoglycemia, and hyperglycemia, respectively;, High Blood Glucose Index;, Hyperglycemic Index;, Hypoglycemic Index;, Index of Glucose Control;, Interquartile Range;, Low Blood Glucose Index;, Mean Amplitude of Glycemic Excursions; dm, of daily s; w, of daily s (within-day variability);,, and, percentage of values in target, below target, and above target, respectively.

3 Supplementary Table S2. Dataset 2 (16 Continuous Glucose Monitoring Time-Series) w dm w dm M , Average Daily Risk Range;, Blood Glucose Risk Index;, percentage coefficient of variation;, Glycemic Risk Assessment Diabetes Equation;,, and, percentages of score due to euglycemia, hypoglycemia, and hyperglycemia, respectively;, High Blood Glucose Index;, Hyperglycemic Index;, Hypoglycemic Index;, Index of Glucose Control;, Interquartile Range;, Low Blood Glucose Index;, Mean Amplitude of Glycemic Excursions; dm, of daily s; w, of daily s (within-day variability);,, and, percentage of values in target, below target, and above target, respectively.

4 Supplementary Table S3. Dataset (33 Continuous Glucose Monitoring Time-Series) w dm w dm J - index M , Average Daily Risk Range;, Blood Glucose Risk Index;, percentage coefficient of variation;, Glycemic Risk Assessment Diabetes Equation;,, and, percentages of score due to euglycemia, hypoglycemia, and hyperglycemia, respectively;, High Blood Glucose Index;, Hyperglycemic Index;, Hypoglycemic Index;, Index of Glucose Control;, Interquartile Range;, Low Blood Glucose Index;, Mean Amplitude of Glycemic Excursions; dm, of daily s; w, of daily s (within-day variability);,, and, percentages of values in target, below target, and above target, respectively.

5 Supplementary Table S4. Example of How the Lack of Indices Normalization Can Lead to Inconsistencies in the Results Dataset, PC 1 PC 2 Dataset 1 (81%) Dataset 1 (91%) Dataset 2 (62%) Dataset 2 (73%) Dataset (60%) Dataset (83%) In particular, glycemic variability indices selected by sparse principal component (PC) for each PC for datasets 1, 2, and are reported. Results change when glycemic variability indices are derived from continuous glucose monitoring time-series expressed in mmol/l (right columns) rather than in mg/dl (left columns)., Average Daily Risk Range;, Blood Glucose Risk Index;, percentage coefficient of variation;,, and, percentages of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, hyperglycemia, and hypoglycemia, respectively;, High Blood Glucose Index;, Index of Glucose Control;, Low Blood Glucose Index;, Mean Amplitude of Glycemic Excursions.