Biomedical subjects

Andrea Caumo

Publications and source records attributed to Andrea Caumo.

21 records · Page 2Linked to original sources

PubMed2002

Amino acid kinetics during the anhepatic phase of liver transplantation.

Alanine and glutamine are interorgan nitrogen/carbon carriers for ureagenesis and gluconeogenesis, which are mainly but not necessarily only hepatic. The liver is central to alanine and glutamine metabolism, but most organs can produce and use them. We studied amino acid kinetics after liver removal to depict initial events of liver failure and to provide a model to study extrahepatic gluconeogenesis and nitrogen disposal in humans. We measured amino acid kinetics with [5,5,5-(2)H(3)]leucine and [3-(13)C]alanine or [1,2-(13)C(2)]glutamine tracers in 21 subjects during and after the anhepatic phase of liver transplantation: 12 were at 7 months posttransplantation, and 7 were healthy control subjects. Anhepatic leucine kinetics, including proteolysis, was unchanged. Alanine plasma and whole-body contents increased 3x and 2x, with a halved metabolic clearance and a doubled production, 2% greater than disposal. Free whole-body glutamine decreased 25% but increased 50% in plasma. Glutamine clearance was halved, and the production decreased by 25%, still 2% greater than disposal. Liver replacement decreased alanine and glutamine concentrations, leaving leucine unchanged. Liver removal caused doubled alanine fluxes, minor changes in glutamine, and no changes in leucine. The initial events after liver removal are an accumulation of three-carbon compounds, an acceleration of alanine turnover, and limited nitrogen storage in alanine and glutamine.

Alanine↗

PubMed2002

Fasting blood sample-based assessment of insulin sensitivity in kidney-pancreas-transplanted patients.

OBJECTIVE: To ascertain whether simple indexes of insulin sensitivity based on a fasting blood sample may be reliable measures of insulin sensitivity in combined kidney-pancreas- transplanted patients. RESEARCH DESIGN AND METHODS: Estimates of insulin sensitivity based on fasting plasma glucose, insulin (homeostasis model assessment of insulin sensitivity [HOMA-IS], Quantitative Insulin Sensitivity Check Index [QUICKI]), and free fatty acid (revised QUICKI) concentrations were compared with insulin sensitivity as assessed with the gold standard technique (euglycemic-hyperinsulinemic clamp) in 22 patients who had undergone kidney-pancreas transplantation (KP-Tx) and 18 matched healthy subjects (NOR). RESULTS: In KP-Tx patients, indexes based on the glucose-insulin product, HOMA-IS (r = 0.47, P = 0.03) and QUICKI (r = 0.47, P = 0.03), were shown to be reliable measures of insulin sensitivity. The introduction of fasting plasma free fatty acid concentration in the revised QUICKI (r = 0.76, P < 0.0001) considerably improved the power of prediction of the clamp-based measure of insulin sensitivity as observed in the healthy control subjects (r = 0.83, P < 0.0001). CONCLUSIONS: This study shows that in KP-Tx patients, HOMA-IS and QUICKI are reliable measures of insulin sensitivity; the additional incorporation of fasting plasma free fatty acid concentration into the glucose-insulin product (revised QUICKI) resulted in a considerably more powerful index.

Adult↗

PubMedDate not supplied

Generalization of map estimation in SAAM II: validation against ADAPT II in a glucose model case study.

Bayesian approaches to model identification [e.g., maximum a posteriori (MAP) estimation] are receiving increasing attention in metabolism since important quantitative knowledge has become available in the last decades, e.g., from tracer experiments. By suitably exploiting this knowledge, more complex physiological models than those solely based on experimental data (Fisherian approach) become resolvable. While ADAPT II is the reference software for MAP estimation in pharmacokinetic/pharmacodynamic/metabolic system analysis, another popular, user-friendly and state-of-the-art software is SAAM II. However, SAAM II does not handle a priori information on correlation among parameters, thus allowing a limited version of MAP estimation to be performed. The aim here is twofold. First, we show that this limitation of SAAM II can be easily overcome by resorting to a probability theory result. Second, we test SAAM II vs ADAPT II implementation of MAP estimation in a real case study: the Bayesian identification of a recently proposed two-compartment minimal model of glucose kinetics during an intravenous glucose tolerance test. SAAM II MAP estimates of glucose effectiveness (SG) and insulin sensitivity (S(I)) obtained in a group of 22 healthy humans are in excellent agreement with those of ADAPT II: S(G) = 2.84 +/- 0.27 vs. 2.84 +/- 0.27 (mlmin(-1) kg(-1), mean +/- SD) and S(I) = 11.46 +/- 1.69 vs. 11.47 +/- 1.69 [10(-2) ml kg(-1) min(-1)/ (microU ml(-1))]. The SAAM II vs. ADAPT II estimates are virtually identical (P > 0.44 and 0.68 for S(G) and S(I), respectively) and also closely correlated (p = 0.9998 and 0.9999).

Algorithms↗