Jia Zhu1,2, Lisa K Volkening1, Lori M Laffel3,2. 1. Pediatric, Adolescent, and Young Adult Section, Section on Clinical, Behavioral and Outcomes Research, Joslin Diabetes Center, Boston, MA. 2. Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, MA. 3. Pediatric, Adolescent, and Young Adult Section, Section on Clinical, Behavioral and Outcomes Research, Joslin Diabetes Center, Boston, MA lori.laffel@joslin.harvard.edu.
Abstract
OBJECTIVE: To evaluate glycemia and metrics of glucose variability in youth with type 1 diabetes, and to assess patterns of 24-h glucose variability according to pubertal status. RESEARCH DESIGN AND METHODS: Metrics of glycemia, glucose variability, and glucose patterns were assessed by using 4 weeks of continuous glucose monitoring (CGM) data from 107 youth aged 8-17 years with type 1 diabetes for ≥1 year. Glucose values per hour were expressed as percentages relative to the mean glucose over 24 h for a 4-week period. Glucose data were compared on the basis of pubertal status-prepubertal (Tanner stage [T] 1), pubertal (T2-4), and postpubertal (T5)-and A1C categories (<7.5% [<58 mmol/mol], ≥7.5% [≥58 mmol/mol]). RESULTS: Youth (50% female, 95% white) had a mean ± SD age of 13.1 ± 2.6 years, diabetes duration of 6.3 ± 3.5 years, and A1C of 7.8 ± 0.8% (62 ± 9 mmol/mol); 88% were pump treated. Prepubertal youth had a higher mean glucose SD (86 ± 12 mg/dL [4.8 ± 0.7 mmol/L]; P = 0.01) and coefficient of variation (CV) (43 ± 5%; P = 0.06) than did pubertal (SD 79 ± 13 mg/dL [4.4 ± 0.7 mmol/L]; CV 41 ± 5%) and postpubertal (SD 77 ± 14 mg/dL [4.3 ± 0.8 mmol/L]; CV 40 ± 5%) youth. Over 24 h, prepubertal youth had the largest excursions from mean glucose and the highest CV across most hours compared with pubertal and postpubertal youth. Across all youth, CV was strongly correlated with the percentage of time with glucose <70 mg/dL (<3.9 mmol/L) (r = 0.79; P < 0.0001). CONCLUSIONS: Prepubertal youth had greater glucose variability independent of A1C than did pubertal and postpubertal youth. A1C alone does not capture the full range of glycemic parameters, highlighting the added insight of CGM in managing youth with type 1 diabetes.
OBJECTIVE: To evaluate glycemia and metrics of glucose variability in youth with type 1 diabetes, and to assess patterns of 24-h glucose variability according to pubertal status. RESEARCH DESIGN AND METHODS: Metrics of glycemia, glucose variability, and glucose patterns were assessed by using 4 weeks of continuous glucose monitoring (CGM) data from 107 youth aged 8-17 years with type 1 diabetes for ≥1 year. Glucose values per hour were expressed as percentages relative to the mean glucose over 24 h for a 4-week period. Glucose data were compared on the basis of pubertal status-prepubertal (Tanner stage [T] 1), pubertal (T2-4), and postpubertal (T5)-and A1C categories (<7.5% [<58 mmol/mol], ≥7.5% [≥58 mmol/mol]). RESULTS: Youth (50% female, 95% white) had a mean ± SD age of 13.1 ± 2.6 years, diabetes duration of 6.3 ± 3.5 years, and A1C of 7.8 ± 0.8% (62 ± 9 mmol/mol); 88% were pump treated. Prepubertal youth had a higher mean glucose SD (86 ± 12 mg/dL [4.8 ± 0.7 mmol/L]; P = 0.01) and coefficient of variation (CV) (43 ± 5%; P = 0.06) than did pubertal (SD 79 ± 13 mg/dL [4.4 ± 0.7 mmol/L]; CV 41 ± 5%) and postpubertal (SD 77 ± 14 mg/dL [4.3 ± 0.8 mmol/L]; CV 40 ± 5%) youth. Over 24 h, prepubertal youth had the largest excursions from mean glucose and the highest CV across most hours compared with pubertal and postpubertal youth. Across all youth, CV was strongly correlated with the percentage of time with glucose <70 mg/dL (<3.9 mmol/L) (r = 0.79; P < 0.0001). CONCLUSIONS: Prepubertal youth had greater glucose variability independent of A1C than did pubertal and postpubertal youth. A1C alone does not capture the full range of glycemic parameters, highlighting the added insight of CGM in managing youth with type 1 diabetes.
Authors: Roy W Beck; Richard M Bergenstal; Peiyao Cheng; Craig Kollman; Anders L Carlson; Mary L Johnson; David Rodbard Journal: J Diabetes Sci Technol Date: 2019-01-13
Authors: Thomas Danne; Revital Nimri; Tadej Battelino; Richard M Bergenstal; Kelly L Close; J Hans DeVries; Satish Garg; Lutz Heinemann; Irl Hirsch; Stephanie A Amiel; Roy Beck; Emanuele Bosi; Bruce Buckingham; Claudio Cobelli; Eyal Dassau; Francis J Doyle; Simon Heller; Roman Hovorka; Weiping Jia; Tim Jones; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; David Maahs; Helen R Murphy; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; Banshi Saboo; Mauro Scharf; William V Tamborlane; Stuart A Weinzimer; Moshe Phillip Journal: Diabetes Care Date: 2017-12 Impact factor: 19.112
Authors: Nancy Elbarbary; Othmar Moser; Saif Al Yaarubi; Hussain Alsaffar; Adnan Al Shaikh; Ramzi A Ajjan; Asma Deeb Journal: Diab Vasc Dis Res Date: 2021 Nov-Dec Impact factor: 3.291
Authors: L A DiMeglio; L G Kanapka; D J DeSalvo; B J Anderson; K R Harrington; M E Hilliard; L M Laffel; W V Tamborlane; M A Van Name; R P Wadwa; S M Willi; S Woerner; J C Wong; K M Miller Journal: Diabet Med Date: 2020-03-17 Impact factor: 4.213