Bayesian Identification of a Population Compartmental Model of C-Peptide Kinetics

Paolo Magni, Riccardo Bellazzi, Giovanni Sparacino, Claudio Cobelli

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)812-823
Number of pages12
JournalAnnals of Biomedical Engineering
Volume28
Issue number7
Publication statusPublished - 2000

Fingerprint

Peptides
Identification (control systems)
Kinetics
Kinetic parameters
Insulin
Cost effectiveness
Medical problems
Markov processes
Experiments
Health
Acoustic waves

Keywords

  • Bayes estimation
  • C-peptide
  • Compartmental model
  • Insulin
  • Markov chain Monte Carlo
  • Population model
  • System identification

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Bayesian Identification of a Population Compartmental Model of C-Peptide Kinetics. / Magni, Paolo; Bellazzi, Riccardo; Sparacino, Giovanni; Cobelli, Claudio.

In: Annals of Biomedical Engineering, Vol. 28, No. 7, 2000, p. 812-823.

Research output: Contribution to journalArticle

Magni, Paolo ; Bellazzi, Riccardo ; Sparacino, Giovanni ; Cobelli, Claudio. / Bayesian Identification of a Population Compartmental Model of C-Peptide Kinetics. In: Annals of Biomedical Engineering. 2000 ; Vol. 28, No. 7. pp. 812-823.
@article{46cac25302ba43c0abea6543ec6c4660,
title = "Bayesian Identification of a Population Compartmental Model of C-Peptide Kinetics",
abstract = "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.",
keywords = "Bayes estimation, C-peptide, Compartmental model, Insulin, Markov chain Monte Carlo, Population model, System identification",
author = "Paolo Magni and Riccardo Bellazzi and Giovanni Sparacino and Claudio Cobelli",
year = "2000",
language = "English",
volume = "28",
pages = "812--823",
journal = "Annals of Biomedical Engineering",
issn = "0090-6964",
publisher = "Springer Netherlands",
number = "7",

}

TY - JOUR

T1 - Bayesian Identification of a Population Compartmental Model of C-Peptide Kinetics

AU - Magni, Paolo

AU - Bellazzi, Riccardo

AU - Sparacino, Giovanni

AU - Cobelli, Claudio

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

KW - Bayes estimation

KW - C-peptide

KW - Compartmental model

KW - Insulin

KW - Markov chain Monte Carlo

KW - Population model

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=0034230481&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034230481&partnerID=8YFLogxK

M3 - Article

C2 - 11016418

AN - SCOPUS:0034230481

VL - 28

SP - 812

EP - 823

JO - Annals of Biomedical Engineering

JF - Annals of Biomedical Engineering

SN - 0090-6964

IS - 7

ER -