title: Using a multi-level B-spline model to analyze and compare patient glucose profiles based on continuous monitoring data.
abstract: OBJECTIVE
  We show how continuous glucose monitoring (CGM) data can be analyzed using a three-level B-spline model, facilitating the estimation of inter-patient variability, within-patient inter-day variability, and measurement error. We propose methods for statistical comparison of glucose profiles among patient groups.
  
  
  METHODS
  We applied a three-level random effects model using quadratic B-spline functions to analyze inter-patient and within-patient inter-day variations of the glucose trend. The estimated SD values of the glucose curves are time-dependent and were averaged over a 24-h period. We analyzed CGM data from 322 patients with type 1 diabetes, 223 patients with type 2 diabetes, and 86 subjects without diabetes using interstitial glucose levels measured every 5 min, for approximately 8 days per patient. We compared group-wide glucose profiles from the insulin pump-treated (n = 124) and multiple daily injection (MDI)-treated (n = 144) patients with type 1 diabetes.
  
  
  RESULTS
  The average inter-patient SD values were 49 mg/dL, 43 mg/dL, and 15 mg/dL for type 1 diabetes patients, type 2 diabetes patients, and subjects without diabetes, respectively. The average within-patient, inter-day SD values were 67 mg/dL, 41 mg/dL, and 18 mg/dL, respectively. The residual SD values were 19 mg/dL, 14 mg/dL, and 8 mg/dL, respectively. We identified a statistically significant difference in glucose profiles during the morning between insulin pump-treated and MDI-treated type 1 diabetes patients.
  
  
  CONCLUSIONS
  B-spline models facilitate the analysis of CGM data and show that type 1 diabetes is associated with higher inter-day glucose variation than type 2 diabetes or being without diabetes. Pump therapy and MDI have different effects on glucose control during specific time periods.
authors.affiliation: Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA. hzheng1@partners.org
authors.forename: Hui
authors.initials: H
authors.lastname: Zheng
authors.forename: David M
authors.initials: DM
authors.lastname: Nathan
authors.forename: David A
authors.initials: DA
authors.lastname: Schoenfeld
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis; D002940:Circadian Rhythm; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy / Q000378:metabolism*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000378:metabolism*; D045604:Extracellular Fluid / Q000378:metabolism*; D005260:Female; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use; D007332:Insulin Infusion Systems; D007700:Kinetics; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory; D015203:Reproducibility of Results; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D007328:Insulin; D005947:Glucose