Biomedical subjects

Giovanni Sparacino

Publications and source records attributed to Giovanni Sparacino.

10 recordsLinked to original sources

PubMed2006

A new dynamic index of insulin sensitivity.

Insulin sensitivity is a crucial parameter of glucose metabolism. The standard measures of insulin sensitivity obtained by an euglycaemic hyperinsulinaemic clamp, Si(clamp), or by the minimal model (MM), SI, do not account for the dynamics of insulin action, i.e., how fast or slow insulin action reaches its plateau value. This is an important physiological information. In this paper we formally define a new insulin sensitivity index which also incorporates information on the dynamics of insulin action, SD(I), show its properties, and exemplify how it can be measured both with the clamp and the MM method. Then, by resorting to real and synthetic data, we show both in IVGTT MM and clamp studies why this new index SD(I) offers, in comparison with SI, a more comprehensive picture of the control of insulin on glucose.

Blood Glucose↗

PubMed2005

Reduced sampling schedule for the glucose minimal model: importance of Bayesian estimation.

The minimal model (MM) of glucose kinetics during an intravenous glucose tolerance test (IVGTT) is widely used in clinical studies to measure metabolic indexes such as glucose effectiveness (S(G)) and insulin sensitivity (S(I)). The standard (frequent) IVGTT sampling schedule (FSS) for MM identification consists of 30 points over 4 h. To facilitate clinical application of the MM, reduced sampling schedules (RSS) of 13-14 samples have also been derived for normal subjects. These RSS are especially appealing in large-scale studies. However, with RSS, the precision of S(G) and S(I) estimates deteriorates and, in certain cases, becomes unacceptably poor. To overcome this difficulty, population approaches such as the iterative two-stage (ITS) approach have been recently proposed, but, besides leaving some theoretical issues open, they appear to be oversized for the problem at hand. Here, we show that a Bayesian methodology operating at the single individual level allows an accurate determination of MM parameter estimates together with a credible measure of their precision. Results of 16 subjects show that, in passing from FSS to RSS, there are no significant changes of point estimates in nearly all of the subjects and that only a limited deterioration of parameter precision occurs. In addition, in contrast with the previously proposed ITS method, credible confidence intervals (e.g., excluding negative values) are obtained. They can be crucial for a subsequent use of the estimated MM parameters, such as in classification, clustering, regression, or risk analysis.

Adult↗

PubMed2004

Insulin minimal model indexes and secretion: proper handling of uncertainty by a Bayesian approach.

The identification of the insulin minimal model (MM) for the estimation of insulin secretion rate (ISR) and physiological indexes (e.g. beta-cell sensitivity) requires the knowledge of C-peptide (CP) kinetics. The four parameters of the two-compartment model of CP kinetics in a given individual can be derived either from an additional bolus experiment or, more frequently, from a population model. However, in both situations, the CP kinetics is uncertain and, in MM identification, it should be treated as such. This paper shows how to handle CP kinetics uncertainty by using a Bayesian methodology. In seven subjects, MM indexes and ISR were estimated together with their confidence intervals, using either the bolus data or the population model to assess CP kinetics. The two main results that arise from the application of the new methodology are: (i) the use of the population model in place of the bolus data to determine CP kinetics does not affect, on average, the point estimates of ISR profile and MM parameters but only the confidence intervals which becomes wider (less than 50%); (ii) in both the bolus and population situation neglecting the uncertainty of CP kinetics, as done in MM literature so far, introduces no bias, on average, on point estimates of MM indexes but only an underestimation of confidence intervals.

Adult↗

PubMed2004

"Population" approach improves parameter estimation of kinetic models from dynamic PET data.

Kinetic modeling is used to indirectly measure physiological parameters from dynamic positron emission tomography (PET) data. Usually, the unknown parameters of the model are estimated, in any given region of interest (ROI), by least squares (LS). However, when the signal-to-noise ratio (SNR) of PET data is too low, LS does not allow reliable parameter estimation. To overcome this problem, we study in this paper the applicability of approaches originally developed in the pharmacokinetic/pharmacodynamic literature and referred to as "population approaches." In particular, we consider the iterative two stage (ITS) method, which, given a set of M ROIs drawn on PET images of a given individual, estimates the unknown model parameters of each ROI by exploiting the information contained in all the M ROIs. After having revised the theory behind ITS, we assess its performance versus LS by using Monte Carlo simulations which allow us to evaluate the bias of the two methods in a variety of situations. Then, we compare the performance of LS and ITS in two case studies on [18F]FDG kinetics in human skeletal muscle. Both simulated and real case studies results show that a population approach is of potential in modeling PET images since it allows to reliably estimate model parameters also in those ROIs where either a bad SNR or a poor sampling (e.g., infrequent scanning and/or short experiment duration) make the use of LS unsuccessful.

Algorithms↗

PubMed2003

Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation.

The so-called minimal model (MM) of glucose kinetics is widely employed to estimate insulin sensitivity (S(I)) both in clinical and epidemiological studies. Usually, MM is numerically identified by resorting to Fisherian parameter estimation techniques, such as maximum likelihood (ML). However, unsatisfactory parameter estimates are sometimes obtained, e.g. S(I) estimates virtually zero or unrealistically high and affected by very large uncertainty, making the practical use of MM difficult. The first result of this paper concerns the mathematical demonstration that these estimation difficulties are inherent to MM structure which can expose S(I) estimation to the risk of numerical non-identifiability. The second result is based on simulation studies and shows that Bayesian parameter estimation techniques are less sensitive, in terms of both accuracy and precision, than the Fisherian ones with respect to these difficulties. In conclusion, Bayesian parameter estimation can successfully deal with difficulties of MM identification inherently due to its structure.

Bayes Theorem↗

PubMed2002

WINSTODEC: a stochastic deconvolution interactive program for physiological and pharmacokinetic systems.

Deconvolution allows the reconstruction of non-accessible inputs (e.g. hormone secretion rate) from their causally-related measurable effects (e.g. hormone plasma concentration). Deconvolution is challenging under several aspects both general (e.g. determination of a suitable trade-off between data fit and solution smoothness in order to contrast ill-conditioning, assessment of the confidence intervals) as well as specific of physiological systems (e.g. non-uniform and infrequent data sampling). Recently, a stochastic regularization approach has been proposed and validated to handle these difficulties (De Nicolao et al., Automatica 33 (1997) 851-870). In this paper, an interactive program, WINSTODEC, is presented to allow the clinical investigator to easily obtain the solution of a deconvolution problem by this approach.

Humans↗

PubMed2002

A Bayesian approach to estimate evoked potentials.

Several approaches, based on different assumptions and with various degree of theoretical sophistication and implementation complexity, have been developed for improving the measurement of evoked potentials (EP) performed by conventional averaging (CA). In many of these methods, one of the major challenges is the exploitation of a priori knowledge. In this paper, we present a new method where the 2nd-order statistical information on the background EEG and on the unknown EP, necessary for the optimal filtering of each sweep in a Bayesian estimation framework, is, respectively, estimated from pre-stimulus data and obtained through a multiple integration of a white noise process model. The latter model is flexible (i.e. it can be employed for a large class of EP) and simple enough to be easily identifiable from the post-stimulus data thanks to a smoothing criterion. The mean EP is determined as the weighted average of the filtered sweeps, where each weight is inversely proportional to the expected value of the norm of the correspondent filter error, a quantity determinable thanks to the employment of the Bayesian approach. The performance of the new approach is shown on both simulated and real auditory EP. A signal-to-noise ratio enhancement is obtained that can allow the (possibly automatic) identification of peak latencies and amplitudes with less sweeps than those required by CA. For cochlear EP, the method also allows the audiology investigator to gather new and clinically important information. The possibility of handling single-sweep analysis with further development of the method is also addressed.

Bayes Theorem↗

PubMed2002

Handling non-negativity in deconvolution of physiological signals: a nonlinear stochastic approach.

A stochastic interpretation of Tikhonov regularization has been recently proposed to attack some open problems of deconvolution when dealing with physiological systems, i.e., in addition to ill-conditioning, infrequent and nonuniform sampling and necessity of having credible confidence intervals. However, the possible violation of the non-negativity constraint cannot be dealt with on firm statistical grounds, since the model of the unknown signal is compatible with negative realizations. In this paper, we propose a new model of the unknown input which excludes negative values. The model is embedded within a Bayesian estimation framework to calculate, by resorting to a Markov chain Monte Carlo algorithm, a nonlinear estimate of the unknown input given by its a posteriori expected value. Applications to simulated and real hormone secretion/pharmacokinetic problems are presented which show that this nonlinear approach is more accurate than the linear one. In addition, more realistic confidence intervals are obtained.

Algorithms↗

PubMed2002

Double isotope tracer method for measuring fractional zinc absorption: theoretical analysis.

Several approaches for estimation of fractional zinc absorption (FZA) by calculating the ratio of oral to intravenous stable isotopic tracer concentrations (at an appropriate time) in urine or plasma after their simultaneous administration have been proposed in the last decade. These simple-to-implement approaches, often referred to as the double isotopic tracer ratio (DITR) method, are more attractive than the classical "deconvolution" method and the more commonly used single-tracer methods based on fecal monitoring and indicator dilution, after oral or intravenous tracer administration, respectively. However, the domain of validity of DITR for measuring FZA has recently been questioned. In this paper, we provide a theoretical justification of the validity of four different "approximate" formulations of the DITR technique by demonstrating mathematically that their accuracy is a consequence of the particular properties of zinc kinetics.

Absorption↗

PubMed2002

Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals.

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.

Bayes Theorem↗