Is the Corneal Thickness Profile Altered in Diabetes Mellitus?

Ramm, Lisa; Spoerl, Eberhard; Pillunat, Lutz E; Terai, Naim

doi:10.1080/02713683.2020.1741009

Cited by 8 publications

(9 citation statements)

References 48 publications

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“…In agreement, an earlier investigation found no differences in absolute peripheral corneal thickness values between healthy subjects and those with diabetes. However, intraindividual difference between thinnest corneal thickness and peripheral pachymetry readings has shown significant higher values in individuals with diabetes than in healthy subjects (Ramm et al 2020).…”

Section: Discussionmentioning

confidence: 98%

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

Fieß

Marx-Gross

Wasielica‐Poslednik

et al. 2021

Acta Ophthalmologica

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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 followup (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 AE 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 = À0.24 µm per year, p < 0.001); body height (B = À0.04 µm, p = 0.005); smoking (B = À0.72 µm, p < 0.001); spherical equivalent (B = À0.70 µm per dioptre, p < 0.001); white-towhite distance (B = À0.75 µm/mm, p < 0.001); mean corneal radius (B = À3.61 µm/mm, p < 0.001); intraocular pressure (B = À0.12 µm/mmHg, p < 0.001); glaucoma (B = À1.94 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.

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Section: Discussionmentioning

confidence: 98%

“…However, intraindividual difference between thinnest corneal thickness and peripheral pachymetry readings has shown significant higher values in individuals with diabetes than in healthy subjects (Ramm et al. 2020).…”

Section: Discussionmentioning

confidence: 99%

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

Fieß

Marx-Gross

Wasielica‐Poslednik

et al. 2021

Acta Ophthalmologica

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“…The corneal endothelium is an ocular structure which may be influenced by systemic conditions, such as diabetes mellitus. Several morphological alterations have been recorded in DM, including reduced cell density, polymorphism, and a higher cell loss rate which correlates with longer disease duration and low glycemic control [ 37 ]. Endothelial cell dysfunction has been described also in the context of hyperlipidemia, smoking or in patients with a history of ischemic stroke [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Particular Anatomy of the Hyperopic Eye and Potential Clinical Implications

Marinescu,

Dascalescu,

Constantin

et al. 2023

Medicina

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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.

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“…ARTh is calculated as the thinnest corneal thickness divided by pachymetric progression to periphery [ 18 ]. Contrarily, Pachyslope is calculated as the difference of mean corneal thickness at ±2.5 mm and corneal thickness at the apex [ 23 , 24 ]. The latest software release has included novel parameters like biomechanical corrected intraocular pressure (bIOP) [ 25 , 26 ]; maximum inverse concave radius (InverseR) [ 18 ]; integrated inverse radius (IntInverseR) [ 18 ]; ratio of central and peripheral deformation in a distance of 1 mm and 2 mm (DAR1/DAR2) [ 18 ] and stiffness parameter at the 1st applanation (SPA1) [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

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

2021

Self Cite

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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 (Sn)/specificity (Sp) of 82%/97%, 73%/81%, 62%/83% and 68%/95% from a validation dataset, respectively. For the RF model, a Sn/Sp 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).

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Is the Corneal Thickness Profile Altered in Diabetes Mellitus?

Cited by 8 publications

References 48 publications

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

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

Particular Anatomy of the Hyperopic Eye and Potential Clinical Implications

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

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