Insulin absorption: a major factor in apparent insulin resistance and the control of type 2 diabetes mellitus.

Our experience over many years from 2 diabetes clinics with large patient populations indicated that, apparently, excessive doses of intermediate-acting insulin preparations (150-300 U of NPH insulin), alone or in combination with rapid-acting insulin, generally did not result in acceptable control of fasting blood glucose. We hypothesized that insulin resistance at the tissue level and the known variability of insulin absorption were not satisfactory explanations. To deal with the ambiguities of available data on insulin absorption, we elected to measure insulin bioavailability via a different approach. Thirteen publications provided plasma insulin concentrations after the subcutaneous administration of defined doses of insulin. These data were then analyzed by noncompartmental analysis and by standard pharmacokinetic methods. Analyses required only knowledge of the areas under the plasma insulin curve and the metabolic clearance rate of insulin. Both of these are parameters measurable with considerable accuracy. Quantitative pharmacokinetic analysis of published insulin absorption curves for insulin administered subcutaneously revealed mean absorption levels for regular and lispro insulin of 70 to 80%, 30% or less for NPH insulin, and 30 to 40% for lente insulin. In conclusion, poor absorption of intermediate-acting insulin preparations, or combinations of intermediate- and rapid-acting insulin preparations, explains the difficulty in lowering blood glucose in patients with type 2 diabetes mellitus who have had long-standing disease, are insulin resistant, and have a flat insulin response to a glucose load.

Precision of neural timing: effects of convergence and time-windowing.

We study the improvement in timing accuracy in a neural system having n identical input neurons projecting to one target neuron. The n input neurons receive the same stimulus but fire at stochastic times selected from one of four specified probability densities, f, each with standard deviation 1.0 msec. The target cell fires if and when it receives m inputs within a time window of epsilon msec. Let sigma(n,m,epsilon) denote the standard deviation of the time of firing of the target neuron (i.e. the standard deviation of the target neuron's latency relative to the arrival time of the stimulus). Mathematical analysis shows that sigma(n,m,epsilon) is a very complicated function of n, m, and epsilon. Typically, sigma(n,m,epsilon) is a non-monotone function of m and epsilon and the improvement of timing accuracy is highly dependent of the shape of the probability density for the time of firing of the input neurons. For appropriate choices of m, epsilon, and f, the standard deviation sigma(n,m,epsilon) may be as low as 1/n. Thus, depending on these variables, remarkable improvements in timing accuracy of such a stochastic system may occur.