The Disposition Index: from Individual to Population Approach.

To correctly evaluate the glucose control system, it is crucial to account for both insulin sensitivity and secretion. The Disposition Index (DI) is the most widely accepted method to do so. The original paradigm (Hyperbolic Law) consists of the multiplicative product of indices related to insulin sensitivity and secretion, but more recently an alternative formula has been proposed, with the exponent α (Power Function Law). Traditionally, curve fitting approaches have been used to evaluate the DI in a population: the algorithmic implementations often introduce some critical issues, such as the assumption that one of the two indices is error-free or the effects of the log-transformation on the measurements errors. In this work, we review the commonly used approaches and show that they provide biased estimates. Then we propose a novel Nonlinear Total Least Square (NLTLS) approach, able to avoid some approximations built in the previously proposed alternatives, and show its superiority. All the traditional fit procedures, including NLTLS, only account for uncertainty affecting insulin sensitivity and secretion indices when they are estimated from noisy data. Thus, they fail when part of the observed variability is due to inherent differences in DI values between individuals. To handle this inevitable source of variability, we propose a NonLinear Mixed-Effects approach that describes the DI using population hyper-parameters such as the population typical values and covariance matrix. On simulated data, this novel technique is much more reliable than the curve fitting approaches, and it proves robust even when no or small population variability is present in the DI values. Applying this new approach to the analysis of real IVGTT data suggests a value of α significantly smaller than 1, supporting the importance of testing the power function law as an alternative to the simpler hyperbolic law.