Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality

TL;DR

This paper develops spatial point process models to describe nerve fiber loss in diabetic neuropathy, revealing that nerve removal patterns evolve from random to dependent as the disease progresses.

Contribution

It introduces a novel spatial thinning model for nerve loss, combining Bayesian inference and goodness-of-fit tests to characterize neuropathy progression.

Findings

Nerve removal initially appears random in early neuropathy.

Dependent thinning models better fit advanced neuropathy data.

Nerve mortality behavior changes with disease progression.

Abstract

Diabetic neuropathy is a disorder characterized by impaired nerve function and reduction of the number of epidermal nerve fibers per epidermal surface. Additionally, as neuropathy related nerve fiber loss and regrowth progresses over time, the two-dimensional spatial arrangement of the nerves becomes more clustered. These observations suggest that with development of neuropathy, the spatial pattern of diminished skin innervation is defined by a thinning process which remains incompletely characterized. We regard samples obtained from healthy controls and subjects suffering from diabetic neuropathy as realisations of planar point processes consisting of nerve entry points and nerve endings, and propose point process models based on spatial thinning to describe the change as neuropathy advances. Initially, the hypothesis that the nerve removal occurs completely at random is tested using independent random thinning of healthy patterns. Then, a dependent parametric thinning model that favors the removal of isolated nerve trees is proposed. Approximate Bayesian computation is used to infer the distribution of the model parameters, and the goodness-of-fit of the models is evaluated using both non-spatial and spatial summary statistics. Our findings suggest that the nerve mortality process changes behaviour as neuropathy advances.