Andersen, Kim Emil

Højbjerre, Malene

The rate by which the insulin is secreted from the pancreatic $\beta$-cells is not directly measurable as part of the insulin is absorbed by the liver before entering the blood stream. However, C-peptide is cosecreted equimolarly and is not absorbed by the liver, implying that reconstruction of the insulin secretion rate (ISR) can be done by solving a highly ill-posed deconvolution problem. We represent the ISR, the C-peptide concentration and the convolution kernel as scaled phase-type densities and develop a Bayesian methodology for estimating such densities via Markov chain Monte Carlo techniques. Hereby closed form evaluation of ISR is possible. We demonstrate the methodology on experimental data from healthy subjects and obtain results which are more realistic than recently reported conclusions based upon methods where the ISR is considered as piecewise constant.The rate by which the insulin is secreted from the pancreatic $\beta$-cells is not directly measurable as part of the insulin is absorbed by the liver before entering the blood stream. However, C-peptide is cosecreted equimolarly and is not absorbed by the liver, implying that reconstruction of the insulin secretion rate (ISR) can be done by solving a highly ill-posed deconvolution problem. We represent the ISR, the C-peptide concentration and the convolution kernel as scaled phase-type densities and develop a Bayesian methodology for estimating such densities via Markov chain Monte Carlo techniques. Hereby closed form evaluation of ISR is possible. We demonstrate the methodology on experimental data from healthy subjects and obtain results which are more realistic than recently reported conclusions based upon methods where the ISR is considered as piecewise constant

Reconstructing the insulin secretion rate by Bayesian deconvolution of phase-type densities

http://vbn.aau.dk/files/837310/R-2005-32.pdf

Reconstructing the insulin secretion rate by Bayesian deconvolution of phase-type densities

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