Is the Corneal Thickness Profile Altered in Diabetes Mellitus?

Association Between Corneal Changes and Retinal Oximetry in Diabetes Mellitus

Purpose

Diabetes mellitus (DM) causes different corneal changes that are associated with the severity of diabetic retinopathy. To identify the pathophysiological reasons for this, corneal tomography and optical densitometry (COD) were combined with retinal oximetry.

Methods

Patients with DM and healthy subjects were included in this pilot study. Spatially resolved corneal thickness and COD were assessed using the Pentacam HR (Oculus). The pachymetry difference (PACDiff) was calculated as an indicator of an increase in the peripheral corneal thickness. Oxygen saturation (SO2) of the retinal vessels was measured using the Retinal Vessel Analyzer (Imedos Systems UG). Subsequently, the associations between corneal and retinal parameters were analyzed.

Results

Data from 30 patients with DM were compared with those from 30 age-matched healthy subjects. In DM, arterial (P = 0.048) and venous (P < 0.001) SO2 levels were increased, and arteriovenous SO2 difference was decreased (P < 0.001). In patients, PACDiff was higher than that in healthy subjects (P < 0.05), indicating a stronger increase in peripheral corneal thickness. The COD was reduced in DM (P = 0.004). The PACDiff of concentric rings with a diameter of 4 mm (r = -0.404; P = 0.033) to 8 mm (r = -0.522; P = 0.004) was inversely correlated with the arteriovenous SO2 difference. Furthermore, PACDiff 4 mm was negatively associated with arterial SO2 (r = -0.389; P = 0.041), and the COD of the peripheral corneal areas correlated positive with arterial SO2 (COD total 10-12 mm: r = 0.408; P = 0.025).

Conclusion

These associations might indicate a common pathogenesis of corneal and retinal changes in DM, which could be caused by reduced oxygen supply, mitochondrial dysfunction, oxidative stress, and cytokine effects.

Particular Anatomy of the Hyperopic Eye and Potential Clinical Implications

Background and Objectives: Hyperopia is a refractive error which affects cognitive and social development if uncorrected and raises the risk of primary angle-closure glaucoma (PACG). Materials and Methods: The study included only the right eye-40 hyperopic eyes in the study group (spherical equivalent (SE) under pharmacological cycloplegia over 0.50 D), 34 emmetropic eyes in the control group (SE between -0.50 D and +0.50 D). A complete ophthalmological evaluation was performed, including autorefractometry to measure SE, and additionally we performed Ocular Response Analyser: Corneal Hysteresis (CH), Corneal Resistance Factor (CRF); specular microscopy: Endothelial cell density (CD), Cell variability (CV), Hexagonality (Hex), Aladdin biometry: Anterior Chamber Depth (ACD), Axial Length (AL), Central Corneal Thickness (CCT). IBM SPSS 26 was used for statistical analysis. Results: The mean age of the entire cohort was 22.93 years (SD ± 12.069), 66.22% being female and 33.78% male. The hyperopic eyes had significantly lower AL, ACD, higher SE, CH, CRF. In the hyperopia group, there are significant, negative correlations between CH and AL (r -0.335), CRF and AL (r -0.334), SE-AL (r -0.593), ACD and CV (r -0.528), CV and CRF (r -0.438), CH (r -0.379), and positive correlations between CCT and CH (r 0.393) or CRF (r 0.435), CD and ACD (r 0.509) or CH (0.384). Age is significantly, negatively correlated with ACD (r -0.447), CH (r -0.544), CRF (r -0.539), CD (r -0.546) and positively with CV (r 0.470). Conclusions: Our study suggests a particular biomechanical behavior of the cornea in hyperopia, in relation with morphological and endothelial parameters. Moreover, the negative correlation between age and ACD suggests a shallower anterior chamber as patients age, increasing the risk for PACG.

Diabetic Retinopathy: Soluble and Imaging Ocular Biomarkers

Diabetic retinopathy (DR), the most common microvascular complication of diabetes mellitus, represents the leading cause of acquired blindness in the working-age population. Due to the potential absence of symptoms in the early stages of the disease, the identification of clinical biomarkers can have a crucial role in the early diagnosis of DR as well as for the detection of prognostic factors. In particular, imaging techniques are fundamental tools for screening, diagnosis, classification, monitoring, treatment planning and prognostic assessment in DR. In this context, the identification of ocular and systemic biomarkers is crucial to facilitate the risk stratification of diabetic patients; moreover, reliable biomarkers could provide prognostic information on disease progression as well as assist in predicting a patient's response to therapy. In this context, this review aimed to provide an updated and comprehensive overview of the soluble and anatomical biomarkers associated with DR.

Peripheral corneal thickness and associated factors - results from the population-based German Gutenberg Health Study

PURPOSE

Changes in peripheral corneal thickness are described in various corneal diseases such as corneal ectasia. However, few data exist describing the increase in corneal thickness from central to peripheral and reporting the normal distribution of corneal thickness in rings around the corneal centre. The aim of this study was to report these cornea characteristics and investigate associated factors in a population-based setting.

METHODS

The Gutenberg Health Study is a prospective, population-based study examining participants in a 5-year follow-up (age range 40-80 years) using Scheimpflug imaging. Corneal thickness was assessed in each participant at the apex, as well as in the corneal centre (thinnest corneal thickness) and in rings with 2, 4, 6, 8 and 10 mm diameter around the corneal centre, and the increase in corneal thickness towards the periphery. The relationship between corneal thickness at these locations and possible associated factors was determined using linear regression models. For this purpose, general and ocular parameters were included.

RESULTS

A total of 9729 participants were included in the present analysis (4874 women, age 59.2 ± 10.8 years). Multivariable analysis showed a correlation between the increase in corneal thickness in the circles from 0 to 10 mm (diameter) and the following parameters: age (B = -0.24 µm per year, p < 0.001); body height (B = -0.04 µm, p = 0.005); smoking (B = -0.72 µm, p < 0.001); spherical equivalent (B = -0.70 µm per dioptre, p < 0.001); white-to-white distance (B = -0.75 µm/mm, p < 0.001); mean corneal radius (B = -3.61 µm/mm, p < 0.001); intraocular pressure (B = -0.12 µm/mmHg, p < 0.001); glaucoma (B = -1.94 µm, p < 0.001); and pseudophakia (B = 0.89 µm, p < 0.001).

CONCLUSION

The results of the present study suggest that several general and ocular parameters are associated with peripheral corneal thickness. In the context of diagnosing glaucoma, a smaller increase in corneal thickness towards the periphery might be a new additional marker.

Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity

Background

To investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.

Methods

Three hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patients who were classified as TKC 1, TKC 2 and TKC 3 were assigned to subgroup mild, moderate, and advanced KC. If patients were classified as TKC 1–2, TKC 2–3 or TKC 3–4, they were assigned to subgroups according to the normative range of further corneal indices (index of surface variance, keratoconus index and minimum radius). Patients classified as TKC 4 were not included in this study due to the limited amount of cases. Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. Data were divided into training (70% of cases) and validation (30% of cases) datasets.

Results

LDA model predicted healthy, mild, moderate, and advanced KC eyes with a sensitivity (S_n)/specificity (S_p) of 82%/97%, 73%/81%, 62%/83% and 68%/95% from a validation dataset, respectively. For the RF model, a S_n/S_p of 91%/94%, 80%/90%, 63%/87%, 72%/95% could be reached for predicting healthy, mild, moderate, and advanced KC eyes, respectively. The overall accuracy of LDA and RF was 71% and 78%, respectively. The accuracy for KC detection including all subgroups of KC severity was 93% in both models.

Conclusion

The RF model showed good accuracy in predicting healthy eyes and various stages of KC. The accuracy was superior with respect to the LDA model. The clinical importance of the models is that the standalone dynamic Scheimpflug tonometry is able to predict the severity of KC without having the keratometric data.

Trial registration

NCT04251143 at Clinicaltrials.gov, registered at 12 March 2018 (Retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40662-021-00244-4.