DDMODEL00000121: Nathan_2008_HbA1c_prediction
Short description:
Linear regression model to describe the relationship between average plasma glucose and HbA1c
PharmML 0.8.x (0.8.1) 



Paolo Magni

Context of model development:  Clinical endpoint; Diagnostic model; 
Discrepancy between implemented model and original publication:  The model is the same of the original publication, even if in the original publication the dependent variable was MPG, while the model here reported uses HbA1C as dependent variable to be compliant with other studies that use this model, e.g. Moller et al. CPT Pharmacometrics Syst Pharmacol. 2014 Jul;3(7):e122.; 
Model compliance with original publication:  Yes; 
Model implementation requiring submitter’s additional knowledge:  No; 
Modelling context description:  OBJECTIVE—The A1C assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Daytoday management is guided by selfmonitoring of capillary glucose concentrations (milligrams per deciliter or millimoles per liter). We sought to define the mathematical relationship between A1C and average glucose (AG) levels and determine whether A1C could be expressed and reported as AG in the same units as used in selfmonitoring. RESEARCH DESIGN AND METHODS—A total of 507 subjects, including 268 patients with type 1 diabetes, 159 with type 2 diabetes, and 80 nondiabetic subjects from 10 international centers, was included in the analyses. A1C levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 3 months. AG was calculated by combining weighted results from at least 2 days of continuous glucose monitoring performed four times, with sevenpoint daily selfmonitoring of capillary (fingerstick) glucose performed at least 3 days per week. RESULTS—Approximately 2,700 glucose values were obtained by each subject during 3 months. Linear regression analysis between the A1C and AG values provided the tightest correlations (AGmg/dl = 28.7 × A1C ? 46.7, R2 = 0.84, P < 0.0001), allowing calculation of an estimated average glucose (eAG) for A1C values. The linear regression equations did not differ significantly across subgroups based on age, sex, diabetes type, race/ethnicity, or smoking status. CONCLUSIONS—A1C levels can be expressed as eAG for most patients with type 1 and type 2 diabetes.; 
Modelling task in scope:  estimation; 
Nature of research:  Clinical research & Therapeutic use; 
Therapeutic/disease area:  Endocrinology; 
Annotations are correct. 

This model is not certified. 
 Additional Files
 Model_Accommodations.txt
 Executable_Nathan_2008_diabetes_MLX_model.txt
 Output_real_Nathan2008_Monolix.txt
 Executable_Nathan_2008_diabetes.mdl
 Output_simulated_Nathan2008.pdf
 DDMODEL00000121.rdf
 Simulated_Nathan_data.csv
 Output_real_Nathan_2008_NONMEM.lst
 Executable_Nathan_2008_diabetes.ctl
 Executable_Nathan_2008_diabetes_MLX_project.mlxtran
 Command.txt
 Model owner: Paolo Magni
 Submitted: Dec 12, 2015 2:55:22 PM
 Last Modified: Oct 10, 2016 8:28:43 PM
Revisions

Version: 8
 Submitted on: Oct 10, 2016 8:28:43 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.

Version: 6
 Submitted on: Jun 2, 2016 7:56:27 PM
 Submitted by: Paolo Magni
 With comment: Model revised without commit message

Version: 2
 Submitted on: Dec 12, 2015 2:55:22 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.
Name
Generated from MDL. MOG ID: Method_2_Nathan_mog
Independent Variables

Function Definitions
$\mathrm{additiveError}:\mathrm{real}\left(\mathrm{additive}:\mathrm{real}\right)=\mathrm{additive}$

Covariate Model: $\mathrm{cm}$
Continuous Covariates
$\mathrm{MPG}$
Parameter Model: $\mathrm{pm}$
Random Variables
${\mathrm{EPS\_1}}_{\mathrm{vm\_err.DV}}~\mathrm{Normal2}\left(\mathrm{mean}=0,\mathrm{var}=1\right)$
Population Parameters
$\mathrm{BETA0\_POP}$
$\mathrm{BETA1\_POP}$
$\mathrm{RES}$
Individual Parameters
$\mathrm{BETA0}=\mathrm{pm.BETA0\_POP}$
$\mathrm{BETA1}=\mathrm{pm.BETA1\_POP}$
Structural Model: $\mathrm{sm}$
Variables
$\mathrm{HBA1C}=\mathrm{pm.BETA0}+\mathrm{pm.BETA1}\cdot \mathrm{cm.MPG}$
Observation Model: $\mathrm{om1}$
Continuous Observation
$Y=\mathrm{sm.HBA1C}+\mathrm{additiveError}\left(\mathrm{additive}=\mathrm{pm.RES}\right)+\mathrm{pm.EPS\_1}$
External Dataset
OID

$\mathrm{nm\_ds}$

Tool Format

NONMEM

File Specification
Format

$\mathrm{csv}$

Delimiter

comma

File Location

Simulated_Nathan_data.csv

Column Definitions
Column ID  Position  Column Type  Value Type 

$\mathrm{ID}$ 
$1$

$\mathrm{id}$

$\mathrm{int}$

$\mathrm{TIME}$ 
$2$

$\mathrm{idv}$

$\mathrm{real}$

$\mathrm{DV}$ 
$3$

$\mathrm{dv}$

$\mathrm{real}$

$\mathrm{MPG}$ 
$4$

$\mathrm{covariate}$

$\mathrm{real}$

$\mathrm{EV}$ 
$5$

$\mathrm{undefined}$

$\mathrm{real}$

Column Mappings
Column Ref  Modelling Mapping 

$\mathrm{TIME}$ 
$T$ 
$\mathrm{DV}$ 
$\mathrm{om1.Y}$ 
$\mathrm{MPG}$ 
$\mathrm{cm.MPG}$ 
Estimation Step
OID

$\mathrm{estimStep\_1}$

Dataset Reference

$\mathrm{nm\_ds}$

Parameters To Estimate
Parameter  Initial Value  Fixed?  Limits 

pm.BETA0_POP 
$1.63$

false

$\left(,\right)$

pm.BETA1_POP 
$0.035$

false

$\left(,\right)$

pm.RES 
$1$

false

$\left(0,\right)$

Operations
Operation: $1$
Op Type

generic

Operation Properties
Name  Value 

algo

$\text{foce}$

Step Dependencies
Step OID  Preceding Steps 

$\mathrm{estimStep\_1}$
