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

Annals of Biomedical Engineering

Volumn 28, Issue 7, 2000, Pages 812-823

  Magni, Paolo ^a   Bellazzi, Riccardo ^a   Sparacino, Giovanni ^b   Cobelli, Claudio ^b  

^a UNIVERSITY OF PAVIA   (Italy)

^b UNIVERSITY OF PADOVA   (Italy)

Author keywords

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

Indexed keywords

C PEPTIDE;

ADULT; ARTICLE; BAYES THEOREM; BODY HEIGHT; BODY SURFACE; BODY WEIGHT; CASE CONTROL STUDY; COMPARATIVE STUDY; COST BENEFIT ANALYSIS; EPIDEMIOLOGY; FEMALE; HUMAN; MALE; METABOLISM; MONTE CARLO METHOD; NON INSULIN DEPENDENT DIABETES MELLITUS; OBESITY; PATHOLOGY; PREDICTION AND FORECASTING; PROBABILITY; STATISTICAL MODEL; TISSUE DISTRIBUTION; VALIDATION STUDY;

ADULT; BAYES THEOREM; BIAS (EPIDEMIOLOGY); BODY HEIGHT; BODY SURFACE AREA; BODY WEIGHT; C-PEPTIDE; CASE-CONTROL STUDIES; COST-BENEFIT ANALYSIS; DIABETES MELLITUS, TYPE 2; FEMALE; HUMANS; LINEAR MODELS; MALE; MARKOV CHAINS; MONTE CARLO METHOD; OBESITY; PREDICTIVE VALUE OF TESTS; TISSUE DISTRIBUTION;

EID: 0034230481     PISSN: 00906964     EISSN: None     Source Type: Journal    
DOI: 10.1114/1.1289459     Document Type: Article

References (27)

1. Bailar, J. C. The promise and problems of meta-analysis. N. Engl. J. Med. 337:1-3, 1997.
2. Beal, S. L., and L. B. Sheiner. Nonmem Users Guide. San Francisco, CA: University of California Press, 1998.
3. Byrne, M. M., J. Sturis, R. J. Sobel, and K. S. Polonsky. Elevated plasma glucose 2h postchallenge predicts defects in beta-cell function. Am. J. Physiol. 270:E572-E579, 1996.
4. Carlin, B. P., and T. A. Louis. Bayes and Empirical Bayes Methods for Data Analysis. London: Chapman & Hall, 1996.
5. Cavaghan, M. K., D. A. Ehrmann, M. M. Byrne, and K. S. Polonsky. Treatment with the oral antidiabetic agent troglita-zone improves beta-cell responses to glucose in subjects with impaired glucose tolerance. J. Clin. Invest. 100:530-537, 1997.
6. Davidian, M., and D. M. Giltinan. Nonlinear Models for Repeated Measurement Data. New York: Chapman & Hall, 1995.
7. Eaton, R. P., R. C. Allen, D. S. Schade, K. M. Erickson, and J. Standefer. Prehepatic insulin production in man: Kinetic analysis using peripheral connecting peptide behaviour. J. Clin. Endocrinol. Metab. 51:520-528, 1980.
8. Faber, O. K., C. Hagen, C. Binder, J. Markussen, V. K. Nahitani, P. M. Blix, H. Kuzuya, D. L. Horwitz, A. H. Rubenstein, and N. Rossing. Kinetics of human connecting peptide in normal and diabetic subjects. J. Clin. Invest. 62:197-202, 1979.
9. Ferrannini, E., and C. Cobelli. The kinetics of insulin in man. I. General aspects. Diabetes Metab. Rev. 3:335-363, 1987.
10. Gelman, A., F. Bois, and J. Jiang. Physiological pharmacokinetic analysis using population modelling and informative prior distributions. J. Am. Stat. Assoc. 91:1400-1412, 1996.
11. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. London: Chapman & Hall, 1995.
12. Geman, S., and D. Geman. Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:721-741, 1984.
13. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. Markov Chain Monte Carlo in Practice. London: Chapman & Hall, 1996.
14. Hastings, W. K.. Monte Carlo sampling methods using Markov Chain and their applications. Biometrika 57:97-109, 1970.
15. Hovorka, R., E. Koukkou, D. Southerden, J. K. Powrie, and M. A. Young. Measuring insulin secretion using a population model of C-peptide kinetics: Accuracy and required sampling schedule. Diabetologia 41:548-554, 1998.
16. Hovorka, R., M. A. Young, and P. A. Soons. ISEC: A program to calculate insulin secretion. Comput. Methods Programs Biomed. 50:253-264, 1996.
17. Magni, P., R. Bellazzi, and G. De Nicolao. Bayesian function learning using MCMC methods. IEEE Trans. Pattern Anal. Mach. Intell. 20:1319-1331, 1998.
18. Mentre, F., and A. Mallet. Handling covariates in population pharmacokinetics. Int. J. Bio-med. Comput. 36:25-33, 1994.
19. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, M. N. Teller, and A. H. Teller. Equations of state calculations by fast computing machine. J. Chem. Phys. 21:1087-1091, 1953.
20. O'Meara, N. M., J. Sturis, E. Van Cauter, and K. S. Polonsky. Lack of control by glucose of ultradian insulin secretory oscillations in impaired glucose tolerance and in non-insulin-dependent diabetes mellitus. J. Clin. Invest. 92:262-271, 1993.
21. Polonsky, K. S., J. Licinio-Paixao, B. D. Given, W. Pugh, P. Rue, J. Galloway, T. Karrison, and B. Frank. Use of biosynthetic human C-peptide in the measurement of insulin secretion rates in normal volunteers and type i diabetic patients. J. Clin. Invest. 77:98-105, 1986.
22. Shapiro, E. T., H. Tillil, A. H. Rubenstein, and K. S. Polonsky. Peripheral insulin parallels changes in insulin secretion more closely than C-peptide after bolus intravenous glucose administration.J. Clin. End. Metab. 67:1094-1099. 1988.
23. Sparacino, G., and C. Cobelli. A stochastic deconvolution method to reconstruct insulin secretion rate after a glucose stimulus. IEEE Trans. Biomed. Eng. 43:512-529, 1996.
24. Toffolo, G., F. De Grandi, and C. Cobelli. Estimation of beta cell sensitivity from IVGTT C-peptide data. Knowledge of the kinetics avoids errors in modeling the secretion. Diabetes 44:845-854, 1995.
25. Van Cauter, E., F. Mestrez, J. Sturis, and K. S. Polonsky. Estimation of insulin secretion rates from C-peptide levels. Diabetes 41:368-377, 1992.
26. Vonesh, E. F.. Nonlinear models for the analysis of longitudinal data. Stat. Med. 11:1929-1954, 1992.
27. Wakefield, J., and J. Bennett. The bayesian modelling of covariates for population pharmcokinetic models. J. Am. Stat. Assoc. 91:917-927, 1996.