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Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals.
Am J Physiol Endocrinol Metab. 2002 Mar; 282(3):E564-73.AJ

Abstract

The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity (S(I)). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic subjects, NLS implemented in MINMOD software often predicts S(I)=0 (the so-called "zero" S(I) problem), whereas general purpose modeling software systems, e.g., SAAM II, provide a very small S(I) but with a very large uncertainty, which produces unrealistic negative values in the confidence interval. To overcome these difficulties, in this article we resort to Bayesian parameter estimation implemented by a Markov chain Monte Carlo (MCMC) method. This approach provides in each individual the S(I) a posteriori probability density function, from which a point estimate and its confidence interval can be determined. Although NLS results are not acceptable in four out of the ten studied subjects, Bayes estimation implemented by MCMC is always able to determine a nonzero point estimate of S(I) together with a credible confidence interval. This Bayesian approach should prove useful in reanalyzing large databases of epidemiological studies.

Authors+Show Affiliations

Dipartimento di Elettronica e Informatica, Università degli Studi di Padova, 35131 Padova, Italy.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

11832358

Citation

Pillonetto, Gianluigi, et al. "Minimal Model S(I)=0 Problem in NIDDM Subjects: Nonzero Bayesian Estimates With Credible Confidence Intervals." American Journal of Physiology. Endocrinology and Metabolism, vol. 282, no. 3, 2002, pp. E564-73.
Pillonetto G, Sparacino G, Magni P, et al. Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals. Am J Physiol Endocrinol Metab. 2002;282(3):E564-73.
Pillonetto, G., Sparacino, G., Magni, P., Bellazzi, R., & Cobelli, C. (2002). Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals. American Journal of Physiology. Endocrinology and Metabolism, 282(3), E564-73.
Pillonetto G, et al. Minimal Model S(I)=0 Problem in NIDDM Subjects: Nonzero Bayesian Estimates With Credible Confidence Intervals. Am J Physiol Endocrinol Metab. 2002;282(3):E564-73. PubMed PMID: 11832358.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals. AU - Pillonetto,Gianluigi, AU - Sparacino,Giovanni, AU - Magni,Paolo, AU - Bellazzi,Riccardo, AU - Cobelli,Claudio, PY - 2002/2/8/pubmed PY - 2002/3/20/medline PY - 2002/2/8/entrez SP - E564 EP - 73 JF - American journal of physiology. Endocrinology and metabolism JO - Am J Physiol Endocrinol Metab VL - 282 IS - 3 N2 - The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity (S(I)). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic subjects, NLS implemented in MINMOD software often predicts S(I)=0 (the so-called "zero" S(I) problem), whereas general purpose modeling software systems, e.g., SAAM II, provide a very small S(I) but with a very large uncertainty, which produces unrealistic negative values in the confidence interval. To overcome these difficulties, in this article we resort to Bayesian parameter estimation implemented by a Markov chain Monte Carlo (MCMC) method. This approach provides in each individual the S(I) a posteriori probability density function, from which a point estimate and its confidence interval can be determined. Although NLS results are not acceptable in four out of the ten studied subjects, Bayes estimation implemented by MCMC is always able to determine a nonzero point estimate of S(I) together with a credible confidence interval. This Bayesian approach should prove useful in reanalyzing large databases of epidemiological studies. SN - 0193-1849 UR - https://www.unboundmedicine.com/medline/citation/11832358/Minimal_model_S_I_=0_problem_in_NIDDM_subjects:_nonzero_Bayesian_estimates_with_credible_confidence_intervals_ L2 - https://journals.physiology.org/doi/10.1152/ajpendo.00576.2000?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -