DDMODEL00000110: Magni_2000_diabetes_C-peptide

  public model
Short description:
Regression model relating the parameters of a two-compartment model of C-peptide kinetics with anthropometric parameters of normal, obese and diabetic subjects via a Bayesian approach.
PharmML 0.8.x (0.8.1)
  • Bayesian identification of a population compartmental model of C-peptide kinetics.
  • Magni P, Bellazzi R, Sparacino G, Cobelli C
  • Annals of biomedical engineering, 7/2000, Volume 28, Issue 7, pages: 812-823
  • Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Italy. paolo.magni@unipv.it
  • When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes 41:368-377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment.
Paolo Magni
Context of model development: Variability sources in PK and PD (CYP, Renal, Biomarkers); Mechanistic Understanding; Clinical end-point;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes 41:368-377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment.;
Modelling task in scope: simulation; estimation;
Nature of research: Clinical research & Therapeutic use;
Therapeutic/disease area: Endocrinology;
Annotations are correct.
This model is not certified.
  • Model owner: Paolo Magni
  • Submitted: Dec 13, 2015 12:54:20 PM
  • Last Modified: Nov 8, 2017 5:24:11 PM
Revisions
  • Version: 18 public model Download this version
    • Submitted on: Nov 8, 2017 5:24:11 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 16 public model Download this version
    • Submitted on: Oct 13, 2016 3:31:43 PM
    • Submitted by: Paolo Magni
    • With comment: Wrong command file in the previous version. Now updated
  • Version: 15 public model Download this version
    • Submitted on: Oct 11, 2016 5:18:02 PM
    • Submitted by: Paolo Magni
    • With comment: Update MDL syntax to the version 1.0 and R script to SEE version 2.0.0. Added prior distributions Code automatically generated/manually modified for WinBUGS
  • Version: 12 public model Download this version
    • Submitted on: Jun 2, 2016 7:07:01 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 4 public model Download this version
    • Submitted on: Dec 13, 2015 12:54:20 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: outputMog

Independent Variables

T

Function Definitions

additiveError:realadditive:real=additive

Covariate Model: cm

Continuous Covariates

HSTATUS
FEMALE
AGE
WEIGHT
HEIGHT

Parameter Model: pm

Random Variables

EPSvm_err.DV~MultivariateNormal2mean=0000precisionmatrix=pm.invOMEGA_PAR

Population Parameters

data1
data_par_joint
par_jointvm_mdl.MDL__prior~RandomSample
mtsn=pm.par_joint1
mtso=pm.par_joint2
mtsd=pm.par_joint3
mFn=pm.par_joint4
mFo=pm.par_joint5
mFd=pm.par_joint6
atl=pm.par_joint7
btl=pm.par_joint8
aVm=pm.par_joint9
bVm=pm.par_joint10
aVf=pm.par_joint11
bVf=pm.par_joint12
invVAR_ts=pm.par_joint13
invVAR_F=pm.par_joint14
invVAR_tl=pm.par_joint15
invVAR_V=pm.par_joint16
invCOV_ts_F=pm.par_joint17
invCOV_ts_tl=pm.par_joint18
invCOV_F_tl=pm.par_joint19
invCOV_ts_V=pm.par_joint20
invCOV_F_V=pm.par_joint21
invCOV_tl_V=pm.par_joint22
invOMEGA_PAR=(pm.invVAR_tspm.invCOV_ts_Fpm.invCOV_ts_tlpm.invCOV_ts_Vpm.invCOV_ts_Fpm.invVAR_Fpm.invCOV_F_tlpm.invCOV_F_Vpm.invCOV_ts_tlpm.invCOV_F_tlpm.invVAR_tlpm.invCOV_tl_Vpm.invCOV_ts_Vpm.invCOV_F_Vpm.invCOV_tl_Vpm.invVAR_V)
BSA=cm.WEIGHT0.425cm.HEIGHT0.7250.20247
GROUP_tl=pm.atl+pm.btlcm.AGE
GROUP_ts={pm.mtsnifcm.HSTATUS=0pm.mtsoifcm.HSTATUS=1pm.mtsdifcm.HSTATUS=2
GROUP_F={pm.mFnifcm.HSTATUS=0pm.mFoifcm.HSTATUS=1pm.mFdifcm.HSTATUS=2
GROUP_V={pm.aVm+pm.bVmpm.BSAifcm.FEMALE=0pm.aVf+pm.bVfpm.BSAotherwise

Individual Parameters

ts_IND=pm.GROUP_ts
F_IND=pm.GROUP_F
tl_IND=pm.GROUP_tl
V_IND=pm.GROUP_V

Structural Model: sm

Variables

ts_PRED=pm.ts_IND
F_PRED=pm.F_IND
tl_PRED=pm.tl_IND
V_PRED=pm.V_IND

Observation Model: om1

Variables

EPS_1=pm.EPS1

Continuous Observation

Y1=sm.ts_PRED+additiveErroradditive=1+EPS_1

Observation Model: om2

Variables

EPS_2=pm.EPS2

Continuous Observation

Y2=sm.F_PRED+additiveErroradditive=1+EPS_2

Observation Model: om3

Variables

EPS_3=pm.EPS3

Continuous Observation

Y3=sm.tl_PRED+additiveErroradditive=1+EPS_3

Observation Model: om4

Variables

EPS_4=pm.EPS4

Continuous Observation

Y4=sm.V_PRED+additiveErroradditive=1+EPS_4

External Dataset

OID
data1

File Specification

Format
csv
Delimiter
comma
File Location
prior_magni2000.csv

Column Definitions

Column ID Position Column Type Value Type
data_mtsn
1
undefined
real
data_mtso
2
undefined
real
data_mtsd
3
undefined
real
data_mFn
4
undefined
real
data_mFo
5
undefined
real
data_mFd
6
undefined
real
data_atl
7
undefined
real
data_btl
8
undefined
real
data_aVm
9
undefined
real
data_bVm
10
undefined
real
data_aVf
11
undefined
real
data_bVf
12
undefined
real
data_invVAR_ts
13
undefined
real
data_invVAR_F
14
undefined
real
data_invVAR_tl
15
undefined
real
data_invVAR_V
16
undefined
real
data_invCOV_ts_F
17
undefined
real
data_invCOV_ts_tl
18
undefined
real
data_invCOV_F_tl
19
undefined
real
data_invCOV_ts_V
20
undefined
real
data_invCOV_F_V
21
undefined
real
data_invCOV_tl_V
22
undefined
real

Column Mappings

Column Ref Modelling Mapping
data_mtsn
pm.data_par_joint1
data_mtso
pm.data_par_joint2
data_mtsd
pm.data_par_joint3
data_mFn
pm.data_par_joint4
data_mFo
pm.data_par_joint5
data_mFd
pm.data_par_joint6
data_atl
pm.data_par_joint7
data_btl
pm.data_par_joint8
data_aVm
pm.data_par_joint9
data_bVm
pm.data_par_joint10
data_aVf
pm.data_par_joint11
data_bVf
pm.data_par_joint12
data_invVAR_ts
pm.data_par_joint13
data_invVAR_F
pm.data_par_joint14
data_invVAR_tl
pm.data_par_joint15
data_invVAR_V
pm.data_par_joint16
data_invCOV_ts_F
pm.data_par_joint17
data_invCOV_ts_tl
pm.data_par_joint18
data_invCOV_F_tl
pm.data_par_joint19
data_invCOV_ts_V
pm.data_par_joint20
data_invCOV_F_V
pm.data_par_joint21
data_invCOV_tl_V
pm.data_par_joint22

External Dataset

OID
nm_ds
Tool Format
NONMEM

File Specification

Format
csv
Delimiter
comma
File Location
Simulated_magni2000_subjects.csv

Column Definitions

Column ID Position Column Type Value Type
ID
1
id
int
TIME
2
idv
real
DV
3
dv
real
DVID
4
dvid
int
HSTATUS
5
covariate
real
FEMALE
6
covariate
real
AGE
7
covariate
real
HEIGHT
8
covariate
real
WEIGHT
9
covariate
real
BMI_DATASET
10
undefined
real
BSA_DATASET
11
undefined
real

Column Mappings

Column Ref Modelling Mapping
TIME
T
DV
{om1.Y1ifDVID=1om2.Y2ifDVID=2om3.Y3ifDVID=3om4.Y4ifDVID=4
HSTATUS
cm.HSTATUS
FEMALE
cm.FEMALE
AGE
cm.AGE
HEIGHT
cm.HEIGHT
WEIGHT
cm.WEIGHT

Simulation Step

OID
simulStep_1

Variable Assignments

Variable Value

Operations

Operation: 1

Op Type
generic
Operation Properties
Name Value

Operation: 2

Op Type
BUGS
Operation Properties
Name Value
niter
1000

Step Dependencies

Step OID Preceding Steps
simulStep_1
 
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