cgm dexcom g4  (Dexcom Inc)

Journal: Communications Medicine

Article Title: Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices

doi: 10.1038/s43856-025-00819-5

Figure Lengend Snippet: a Representative continuous glucose monitoring (CGM) time series data and their corresponding autocorrelation functions from two participants. Red lines indicate the mean autocorrelation values (AC_Mean), with red shading indicating the variance (AC_Var). The autocorrelation was calculated with different time lags, where lag 1 represents the correlation ( R ) between glucose measurements taken 5 min apart (Glucose (t) vs Glucose (t + 5 × 1)), lag 5 represents 25-min intervals (Glucose (t) vs Glucose (t + 5×5)) and lag 15 represents measurements 75-min intervals (Glucose (t) vs Glucose (t + 5 × 15)). b Heatmap of Spearman’s correlation coefficient with P values for testing the hypothesis of no correlation. The analysis is based on data from 64 participants. c Hierarchical clustering of CGM-derived indices (DTW_Mod, DTW_Sev, and AC_Var) in 64 participants using Euclidean distance as a metric with the Ward method. Rows represent individual participants and columns show the standardized values of the CGM-derived indices. d Box plots of oral DI and clamp DI for each cluster. Each point corresponds to the value for a single participant. * P < 0.05. The P values corresponding to the symbols are as follows: Cluster 1 (Oral DI) vs Cluster 3 (Oral DI), 0.038; Cluster 1 (Oral DI) vs Cluster 4 (Oral DI), 0.006; Cluster 1 (Clamp DI) vs Cluster 2 (Clamp DI), 0.034; Cluster 1 (Clamp DI) vs Cluster 4 (Clamp DI), 0.021. e Sankey diagram showing the relationship between cluster assignment and diabetes diagnosis. f Clamp DI values stratified by glycaemic subtypes: NGT_1 (NGT in cluster 1), NGT_2 (NGT in cluster 2 or 4), and IGT. The P values corresponding to the symbols are as follows: NGT_1 (Clamp DI) vs NGT_2 (Clamp DI), 0.047; NGT_1 (Clamp DI) vs IGT (Clamp DI), 0.030. g – i 95% confidence intervals for regression coefficients showing the contributions of: ( g ) DTW_Mod, DTW_Sev and AC_Var to oral DI; ( h ), DTW_Mod, DTW_Sev and AC_Var to clamp DI; and ( i ), CGM_Std and AC_Var to clamp DI. PG120, plasma glucose concentration at 120 min during the oral glucose tolerance test; I.I., insulinogenic index; oral DI, oral disposition index; AUC_IRI, area under insulin curve during the first 10 min of hyperglycemic clamp test; ISI, insulin sensitivity index; clamp DI; clamp disposition index.

Article Snippet: We also performed an analysis using a publicly available dataset of CGM (Dexcom G4 CGM System; Dexcom, Fort Lauderdale, FL, USA), OGTT, and steady-state plasma glucose (SSPG) test outcomes from a previously reported study .

Techniques: Derivative Assay, Biomarker Discovery, Clinical Proteomics, Concentration Assay

Journal: Communications Medicine

Article Title: Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices

doi: 10.1038/s43856-025-00819-5

Figure Lengend Snippet: a A spring layout of the correlation network of CGM-derived indices (red), blood glucose-related indices (magenta), insulin sensitivity, secretion, and clearance-related indices (blue); and other clinical measures (green). Relationships with absolute Spearman’s correlation coefficients of 0.25 or higher are connected with edges. The width of the edges is proportional to the corresponding correlation coefficient. b VIF of all variables. c VIF of each variable remaining after removing the variable with the highest VIF one by one until the VIF of all variables are less than 10. d , e VIP scores from PLS regression for predicting ( d ) oral DI and ( e ) clamp DI. Dotted lines indicate significance threshold (VIP ≥ 1). f , g Relationship between regularization coefficients (Lambda) and the mean squared error (MSE) based on the leave-one-out cross-validation in predicting ( f ) oral DI and ( g ) clamp DI. Dotted vertical lines indicate optimal lambda values. h , i Lasso regularization paths along the Lambda in predicting ( h ) oral DI and ( i ) clamp DI. Cyan, magenta, and gray lines indicate the estimated coefficients of AC_Mean, AC_Var, and the other input variables, respectively. Dotted vertical lines indicate the optimal Lambda. j , k Estimated coefficients at the optimal Lambda in predicting ( j ) oral DI and ( k ) clamp DI. Only variables with non-zero coefficients are shown. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; PG120, plasma glucose concentration at 120 min during the oral glucose tolerance test; I.I., insulinogenic index; oral DI, oral disposition index; AUC_IRI, area under insulin curve during the first 10 min of hyperglycemic clamp test; ISI, insulin sensitivity index; clamp DI; clamp disposition index, VIF; variance inflation factor, VIP; variable importance in projection.

Article Snippet: We also performed an analysis using a publicly available dataset of CGM (Dexcom G4 CGM System; Dexcom, Fort Lauderdale, FL, USA), OGTT, and steady-state plasma glucose (SSPG) test outcomes from a previously reported study .

Techniques: Derivative Assay, Biomarker Discovery, Clinical Proteomics, Concentration Assay

Journal: Communications Medicine

Article Title: Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices

doi: 10.1038/s43856-025-00819-5

Figure Lengend Snippet: a A spring layout of the correlation network of CGM-derived indices (red), blood glucose-related indices (magenta), insulin sensitivity-related index (blue); and other clinical measures (green). Relationships with absolute Spearman’s correlation coefficients of 0.25 or higher are connected with edges. The width of the edges is proportional to the corresponding correlation coefficient. The analysis is based on data from 57 participants. b VIF of all variables. c VIF of each variable remaining after removing the variable with the highest VIF one by one until the VIF of all variables are less than 10. d Comparison of VIF values between the previously reported dataset (VIFp) and the current study dataset (VIFt). Points represent individual indices. Spearman correlation coefficient R is shown with 95% confidence intervals. e VIP scores from the PLS regression predicting SSPG. Dotted line indicates significance threshold (VIP ≥ 1). f Relationship between regularization coefficients (Lambda) and the mean squared error (MSE) based on the leave-one-out cross-validation in predicting SSPG. Dotted vertical lines indicate optimal lambda values. g Lasso regularization paths along the Lambda in predicting SSPG. Cyan, magenta, and gray lines indicate the estimated coefficients of AC_Mean, AC_Var, and the other input variables, respectively. Dotted vertical lines indicate the optimal Lambda. h Estimated coefficients with the optimal Lambda in predicting SSPG. Only variables with non-zero coefficients are shown. BMI, body mass index; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; PG120, plasma glucose concentration at 120 min during the oral glucose tolerance test; SSPG, steady state plasma glucose; VIF, variance inflation factor; VIP, variable importance in projection.

Article Snippet: We also performed an analysis using a publicly available dataset of CGM (Dexcom G4 CGM System; Dexcom, Fort Lauderdale, FL, USA), OGTT, and steady-state plasma glucose (SSPG) test outcomes from a previously reported study .

Techniques: Derivative Assay, Comparison, Biomarker Discovery, Clinical Proteomics, Concentration Assay

Journal: Reviews in Endocrine & Metabolic Disorders

Article Title: Continuous glucose monitoring in patients with inherited metabolic disorders at risk for Hypoglycemia and Nutritional implications

doi: 10.1007/s11154-024-09903-y

Figure Lengend Snippet: Main findings and highlights resulted by the systematic review

Article Snippet: Peeks et al. 2021 [ ] , 1°subset: n.1 GSDIa; 2° subset: n. 11 GSDs, 3° subset: n.3 GSDIb , 1°subset: 9y; 2°subset:2-22y; 3°subset: 2-11y , Rt-CGM , 1°, 3° subsets: Dexcom G6 (Dexcom), non-blinded; 2° subset Dexcom G4 (Dexcom), blinded , 1°subset: 5 days; 2°subset: 1 day; 3°subset: 30 days , 1° subset: case series (single case report); 2° subset: controlled intervention (prospective, randomized, double blind cross-over); 3° subset: pre-post without control group (retrospective) , 1° subset: fair; 2° subset: poor; 3° subset: poor.

Techniques: Control

Journal: Reviews in Endocrine & Metabolic Disorders

Article Title: Continuous glucose monitoring in patients with inherited metabolic disorders at risk for Hypoglycemia and Nutritional implications

doi: 10.1007/s11154-024-09903-y

Figure Lengend Snippet: Rt-CGM/FGM in GSDs: population features, devices and study design

Article Snippet: Peeks et al. 2021 [ ] , 1°subset: n.1 GSDIa; 2° subset: n. 11 GSDs, 3° subset: n.3 GSDIb , 1°subset: 9y; 2°subset:2-22y; 3°subset: 2-11y , Rt-CGM , 1°, 3° subsets: Dexcom G6 (Dexcom), non-blinded; 2° subset Dexcom G4 (Dexcom), blinded , 1°subset: 5 days; 2°subset: 1 day; 3°subset: 30 days , 1° subset: case series (single case report); 2° subset: controlled intervention (prospective, randomized, double blind cross-over); 3° subset: pre-post without control group (retrospective) , 1° subset: fair; 2° subset: poor; 3° subset: poor.

Techniques: Control

Journal: Reviews in Endocrine & Metabolic Disorders

Article Title: Continuous glucose monitoring in patients with inherited metabolic disorders at risk for Hypoglycemia and Nutritional implications

doi: 10.1007/s11154-024-09903-y

Figure Lengend Snippet: Rt-CGM/FGM in CH: population features, devices and study design

Article Snippet: Peeks et al. 2021 [ ] , 1°subset: n.1 GSDIa; 2° subset: n. 11 GSDs, 3° subset: n.3 GSDIb , 1°subset: 9y; 2°subset:2-22y; 3°subset: 2-11y , Rt-CGM , 1°, 3° subsets: Dexcom G6 (Dexcom), non-blinded; 2° subset Dexcom G4 (Dexcom), blinded , 1°subset: 5 days; 2°subset: 1 day; 3°subset: 30 days , 1° subset: case series (single case report); 2° subset: controlled intervention (prospective, randomized, double blind cross-over); 3° subset: pre-post without control group (retrospective) , 1° subset: fair; 2° subset: poor; 3° subset: poor.

Techniques: Biomarker Discovery, Control