(Pre)diabetes and a higher level of glycaemic measures are continuously associated with corneal neurodegeneration assessed by corneal confocal microscopy: the Maastricht Study.

AIMS/HYPOTHESIS: To assess the associations between glucose metabolism status and a range of continuous measures of glycaemia with corneal nerve fibre measures, as assessed using corneal confocal microscopy. METHODS: We used population-based observational cross-sectional data from the Maastricht Study of N=3471 participants (mean age 59.4 years, 48.4% men, 14.7% with prediabetes, 21.0% with type 2 diabetes) to study the associations, after adjustment for demographic, cardiovascular risk and lifestyle factors, between glucose metabolism status (prediabetes and type 2 diabetes vs normal glucose metabolism) plus measures of glycaemia (fasting plasma glucose, 2 h post-load glucose, HbA1c, skin autofluorescence [SAF] and duration of diabetes) and composite Z-scores of corneal nerve fibre measures or individual corneal nerve fibre measures (corneal nerve bifurcation density, corneal nerve density, corneal nerve length and fractal dimension). We used linear regression analysis, and, for glucose metabolism status, performed a linear trend analysis. RESULTS: After full adjustment, a more adverse glucose metabolism status was associated with a lower composite Z-score for corneal nerve fibre measures (ß coefficients [95% CI], prediabetes vs normal glucose metabolism -0.08 [-0.17, 0.03], type 2 diabetes vs normal glucose metabolism -0.14 [-0.25, -0.04]; linear trend analysis showed a p value of 0.001), and higher levels of measures of glycaemia (fasting plasma glucose, 2 h post-load glucose, HbA1c, SAF and duration of diabetes) were all significantly associated with a lower composite Z-score for corneal nerve fibre measures (per SD: -0.09 [-0.13, -0.05], -0.07 [-0.11, -0.03], -0.08 [-0.11, -0.04], -0.05 [-0.08, -0.01], -0.09 [-0.17, -0.001], respectively). In general, directionally similar associations were observed for individual corneal nerve fibre measures. CONCLUSIONS/INTERPRETATION: To our knowledge, this is the first population-based study to show that a more adverse glucose metabolism status and higher levels of glycaemic measures were all linearly associated with corneal neurodegeneration after adjustment for an extensive set of potential confounders. Our results indicate that glycaemia-associated corneal neurodegeneration is a continuous process that starts before the onset of type 2 diabetes. Further research is needed to investigate whether early reduction of hyperglycaemia can prevent corneal neurodegeneration.

Clinically Based Automated Tracing and Tortuosity Estimation of Corneal Nerve Fibers From Confocal Microscopy Images.

PURPOSE: The purpose of this study was the development of an algorithm able to automatically trace corneal nerves and to estimate a nerve tortuosity index that is useful in clinical practice. METHODS: In vivo confocal microscopy is an imaging technique that allows the clinical assessment of corneal and systemic diseases. Many studies have demonstrated a correlation between the tortuosity level of nerve fibers in the subbasal plexus layer and some pathologies. We developed an algorithm that provides fully automatic tracing of nerve fibers. It also includes a new way of dealing with bifurcations, separating the main paths from the secondary ones. Based on this automated tracing, the tortuosity was estimated as the absolute curvature, tortuosity density, and fractal dimension. These metrics were considered first individually and then as a linear combination of 2 or 3 of them. We investigated the capability of the estimated tortuosity to emulate the clinical classification into low, mid, and high tortuosity levels. Furthermore, we investigated its ability to distinguish healthy subjects from pathological subjects. RESULTS: Excellent agreement between manual and automated grouping of tortuosity (96.6% accuracy) was obtained. Moreover, the proposed algorithm could differentiate between healthy and pathological subjects with an accuracy of 77.1% by analyzing each image individually. The accuracy improved to 86.31% by considering 3 images of the same subject simultaneously. CONCLUSIONS: The proposed framework provides completely automated analysis of corneal nerve images. The results demonstrate the ability of our method to emulate the clinical classification of tortuosity levels and its potential for identifying healthy and pathological subjects.

Wide-field mosaics of the corneal subbasal nerve plexus in Parkinson's disease using in vivo confocal microscopy.

In vivo confocal microscopy (IVCM) is a non-invasive imaging technique facilitating real-time acquisition of images from the live cornea and its layers with high resolution (1-2 µm) and high magnification (600 to 800-fold). IVCM is extensively used to examine the cornea at a cellular level, including the subbasal nerve plexus (SBNP). IVCM of the cornea has thus gained intense interest for probing ophthalmic and systemic diseases affecting peripheral nerves. One of the main drawbacks, however, is the small field of view of IVCM, preventing an overview of SBNP architecture and necessitating subjective image sampling of small areas of the SBNP for analysis. Here, we provide a high-quality dataset of the corneal SBNP reconstructed by automated mosaicking, with an average mosaic image size corresponding to 48 individual IVCM fields of view. The mosaic dataset represents a group of 42 individuals with Parkinson's disease (PD) with and without concurrent restless leg syndrome. Additionally, mosaics from a control group (n = 13) without PD are also provided, along with clinical data for all included participants.

Parkinson's disease with restless legs syndrome-an in vivo corneal confocal microscopy study.

Small fiber neuropathy (SFN) has been suggested as a trigger of restless legs syndrome (RLS). An increased prevalence of peripheral neuropathy has been demonstrated in Parkinson's disease (PD). We aimed to investigate, in a cross-sectional manner, whether SFN is overrepresented in PD patients with concurrent RLS relative to PD patients without RLS, using in vivo corneal confocal microscopy (IVCCM) and quantitative sensory testing (QST) as part of small fiber assessment. Study participants comprised of age- and sex-matched PD patients with (n = 21) and without RLS (n = 21), and controls (n = 13). Diagnosis of RLS was consolidated with the sensory suggested immobilization test. Assessments included nerve conduction studies (NCS), Utah Early Neuropathy Scale (UENS), QST, and IVCCM, with automated determination of corneal nerve fiber length (CNFL) and branch density (CNBD) from wide-area mosaics of the subbasal nerve plexus. Plasma neurofilament light (p-NfL) was determined as a measure of axonal degeneration. No significant differences were found between groups when comparing CNFL (p = 0.81), CNBD (p = 0.92), NCS (p = 0.82), and QST (minimum p = 0.54). UENS scores, however, differed significantly (p = 0.001), with post-hoc pairwise testing revealing higher scores in both PD groups relative to controls (p = 0.018 and p = 0.001). Analysis of all PD patients (n = 42) revealed a correlation between the duration of L-dopa therapy and CNBD (ρ = -0.36, p = 0.022), and p-NfL correlated with UENS (ρ = 0.35, p = 0.026) and NCS (ρ = -0.51, p = 0.001). Small and large fiber neuropathy do not appear to be associated with RLS in PD. Whether peripheral small and/or large fiber pathology associates with central neurodegeneration in PD merits further longitudinal studies.

Multiple-Image Deep Learning Analysis for Neuropathy Detection in Corneal Nerve Images.

PURPOSE: Automated classification of corneal confocal images from healthy subjects and diabetic subjects with neuropathy. METHODS: Over the years, in vivo confocal microscopy has established itself as a rapid and noninvasive method for clinical assessment of the cornea. In particular, images of the subbasal nerve plexus are useful to detect pathological conditions. Currently, clinical information is derived through a manual or semiautomated process that traces corneal nerves and achieves their descriptors (eg, density and tortuosity). This is tedious and subjective. To overcome this limitation, a method based on a convolutional neural network (CNN) for the classification of images from healthy subjects and diabetic subjects with neuropathy is proposed. The CNN simultaneously analyzes 3 nonoverlapping images, from the central region of the cornea. The algorithm automatically extracts features, without the need for neither nerve tracing nor parameter extraction nor montage/mosaicking, and provides an overall classification for each image trio. RESULTS: On a dataset composed by images from 50 healthy subjects and 50 subjects with neuropathy, the algorithm achieves a classification accuracy of 96%. The proposed method improves the results obtained using a traditional method that traces nerves and evaluates their density and tortuosity. CONCLUSIONS: The proposed method provides a completely automated analysis of corneal confocal images. Results demonstrate the potentiality of the CNN in identifying clinically useful features for corneal nerves by analysis of multiple images.