Usefulness of Eye Fixation Assessment for Identifying Type 2 Diabetic Subjects at Risk of Dementia

ASSOCIATION OF MACULAR STRUCTURE WITH MICROPERIMETRY SENSITIVITY FOLLOWING VITRECTOMY FOR PROLIFERATE DIABETIC RETINOPATHY

PURPOSE

To evaluate macular sensitivity using microperimetry in patients with proliferate diabetic retinopathy following vitrectomy and to investigate the relationship between the sensitivity and foveal microstructures with optical coherence tomography/angiography.

METHODS

Eighty-four eyes of 84 patients with proliferative diabetic retinopathy, who were indicated for vitrectomy, had no intraocular surgery history 3 months preoperatively, and were able to ensure fundus examination after the vitrectomy, were included. A logMAR best-corrected visual acuity, macular sensitivity of microperimetry, macular retinal thickness, and macular vessel perfusion using optical coherence tomography/angiography were examined at 1 week, 1 month, and 3 months postoperatively.

RESULTS

The logMAR best-corrected visual acuity and mean macular sensitivity of patients with proliferative diabetic retinopathy improved postoperatively (P < 0.05). There was a significant correlation between best-corrected visual acuity and mean sensitivity (P < 0.05). Postoperative mean macular sensitivity was significantly correlated with outer retinal thickness in the 0 to 6 mm macular area (P < 0.05) and also significantly correlated with deep capillary plexus perfusion (P < 0.05). Fixation stability and mean macular sensitivity did not show any correlation with glycated hemoglobin, triglyceride, serum total cholesterol, carbamide, and creatinine and duration of diabetes mellitus (P > 0.05).

CONCLUSION

Postoperative mean macular sensitivity was significantly correlated with outer retinal thickness and deep capillary plexus perfusion for patients with proliferative diabetic retinopathy. The authors found that the visual performance of patients can be evaluated by the outer retinal thickness and deep capillary plexus perfusion, so optical coherence tomography/angiography examination can be an important prognostic factor for visual performance in patients.Clinical Trial Registration: This trial is registered with the Chinese Clinical Trial Registry (http://www.chictr.org.cn; Registration No.: ChiCTR2100043399).

The usefulness of the retina for identifying people with type 2 diabetes with prodromal stages of dementia

Type 2 diabetes (T2D) is associated with cognitive impairment and dementia. The detection of cognitive impairment is important because this population is at higher risk of experiencing difficulties in the self-management of diabetes. Mild cognitive impairment (MCI) often remains undiagnosed due to lack of simple tools for screening at large scale. This represents an important gap in the patients' management because subjects with diabetes and MCI are at high risk of progressing to dementia. Due to its developmental origin as a brain-derived tissue, the retina has been proposed as a potential means of non-invasive and readily accessible exploration of brain pathology. Recent evidence showed that retinal imaging and/or functional tests are correlated with the cognitive function and brain changes in T2D. Simple retinal functional tests (i.e. retinal microperimetry) have proven to be useful as reliable tool for the cognitive evaluation and monitoring in patients with T2D>65 years. This review gives an overall update on the usefulness of retinal imaging in identifying patients with T2D at risk of developing dementia.

A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson's disease

The idea that eye movements can reflect certain aspects of brain function and inform on the presence of neurodegeneration is not a new one. Indeed, a growing body of research has shown that several neurodegenerative disorders, such as Alzheimer's and Parkinson's Disease, present characteristic eye movement anomalies and that specific gaze and eye movement parameters correlate with disease severity. The use of detailed eye movement recordings in research and clinical settings, however, has been limited due to the expensive nature and limited scalability of the required equipment. Here we test a novel technology that can track and measure eye movement parameters using the embedded camera of a mobile tablet. We show that using this technology can replicate several well-known findings regarding oculomotor anomalies in Parkinson's disease (PD), and furthermore show that several parameters significantly correlate with disease severity as assessed with the MDS-UPDRS motor subscale. A logistic regression classifier was able to accurately distinguish PD patients from healthy controls on the basis of six eye movement parameters with a sensitivity of 0.93 and specificity of 0.86. This tablet-based tool has the potential to accelerate eye movement research via affordable and scalable eye-tracking and aid with the identification of disease status and monitoring of disease progression in clinical settings.

Retinal sensitivity and gaze fixation evaluated by microperimetry in subjects with type 2 diabetes: two independent parameters that explore different neuronal circuits

BACKGROUND AND AIMS

Retinal sensitivity (RS) and gaze fixation (GF) assessed by retinal microperimetry are useful and complementary tools for identifying mild cognitive impairment (MCI) in patients with type 2 diabetes (T2D). The hypothesis is that RS and GF examine different neural circuits: RS depends only on the visual pathway while GF reflects white matter complex connectivity networks. The aim of the study is to shed light to this issue by examining the relationship of these two parameters with visual evoked potentials (VEP), the current gold standard to examine the visual pathway.

MATERIALS AND METHODS

Consecutive T2D patients > 65 years were recruited from the outpatient clinic. Retinal microperimetry (MAIA 3rd generation) and visual evoked potentials (VEP) (Nicolet Viking ED). RS (dB), GF (BCEA63%, BCEA95%) (MAIA) and VEP (Latency P100ms, Amplitude75-100 uV) were analyzed.

RESULTS

Thirty three patients (45% women, 72.1 ± 4.6 years) were included. VEP parameters significantly correlated with RS but not with GF.

CONCLUSIONS

These results confirm that RS but not GF depends on the visual pathway, reinforcing the concept that they are complementary diagnostic tools. Used together can further increase the value of microperimetry as screening test for identifying T2D population with cognitive impairment.

New methods for the diagnosis and monitoring of cognitive function in patients with type 2 diabetes

The presence of type 2 diabetes acts as an accelerator of cognitive impairment (mild cognitive impairment and later dementia), with a significant impact on the management of the disease and its complications. Therefore, it is recommended to perform an annual evaluation of cognitive function in patients with diabetes older than 65 years. Current guidelines still recommend the use of the Minimental State Evaluation Test (MMSE) as screening test, but it has a modest sensitivity and specificity for identifying mild cognitive impairment. This represents an important gap because patients with mild cognitive impairment are at risk of progressing to dementia. The neurocognitive diagnosis is based on complex neuropsychological tests, which require specifically trained personnel and are time consuming, making its routine incorporation into daily clinical practice unfeasible. Therefore, at present there are no reliable biomarkers to identify patients with type 2 diabetes at increased risk of developing cognitive impairment. Since the brain and the retina have a common embryological origin, our Research Group, has worked over the last 10 years evaluating the usefulness of the retina as a "window" to the brain. We provided evidence that retinal microperimetry is a simple, feasible and useful tool for screening and monitoring cognitive function in patients with type 2 diabetes. We propose a review of actual tests recommended for screening of cognitive impairment as well as an update of new emerging methods, such as retinal microperimetry.

Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy

Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.

Common pathways in dementia and diabetic retinopathy: understanding the mechanisms of diabetes-related cognitive decline

Type 2 diabetes (T2D) is associated with multiple comorbidities, including diabetic retinopathy (DR) and cognitive decline, and T2D patients have a significantly higher risk of developing Alzheimer's disease (AD). Both DR and AD are characterized by a number of pathological mechanisms that coalesce around the neurovascular unit, including neuroinflammation and degeneration, vascular degeneration, and glial activation. Chronic hyperglycemia and insulin resistance also play a significant role, leading to activation of pathological mechanisms such as increased oxidative stress and the accumulation of advanced glycation end-products (AGEs). Understanding these common pathways and the degree to which they occur simultaneously in the brain and retina during diabetes will provide avenues to identify T2D patients at risk of cognitive decline.

Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis

Background

Diabetic retinopathy (DR) affects 10–24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.

Purpose

The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.

Data sources

A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.

Study selection

Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.

Data extraction

The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.

Data synthesis and conclusion

The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.

Limitations

Selected studies showed a high variation in sample size and quality and quantity of available data.

The Gaze Fixation Assessed by Microperimetry: A Useful Tool for the Monitoring of the Cognitive Function in Patients with Type 2 Diabetes

Current guidelines recommend annual screening for cognitive impairment in patients > 65 years with type 2 diabetes (T2D). The most used tool is the mini-mental state evaluation (MMSE). Retinal microperimetry is useful for detecting cognitive impairment in these patients, but there is no information regarding its usefulness as a monitoring tool. We aimed to explore the role of retinal microperimetry in the annual follow-up of the cognitive function of patients with T2D older than 65 years. Materials and Methods: Prospective observational study, comprising patients > 65 years with T2D, attended at our center between March–October 2019. A complete neuropsychological evaluation assessed the baseline cognitive status (mild cognitive impairment, MCI, or normal, NC). Retinal microperimetry (sensitivity, gaze fixation) and MMSE were performed at baseline and after 12 months. Results: Fifty-nine patients with MCI and 22 NC were identified. A significant decline in the MMSE score was observed after 12 months in the MCI group (25.74 ± 0.9 vs. 24.71 ± 1.4; p = 0.001). While no significant changes in retinal sensitivity were seen, all gaze-fixation parameters worsened at 12 months and significantly correlated with a decrease in the MMSE scores. Conclusion: Retinal microperimetry is useful for the monitoring of cognitive decline in patients > 65 years with T2D. Gaze fixation seems a more sensitive parameter for follow-up after 12 months than retinal sensitivity.

Neuropathic damage in the diabetic eye: clinical implications

In recent years, emerging evidence support that the eye is target of diabetes neuropathy. There are two components of the eye that are mainly involved in the neurodegenerative process induced by diabetes: the retina and the cornea. The study of functional and structural changes in these components of the eye will provide useful information to identify subjects with diabetes at risk of diabetic peripheral neuropathy and dementia. In this review the state of the art regarding the evidence and clinical implications of this emerging concept will be provided. In addition, the relationship between retinal and corneal neurodegeneration with peripheral neuropathy and cognitive decline will be analyzed. Finally, the scientific gaps than need to be covered and will be critically examined.

Understanding multifactorial brain changes in type 2 diabetes: a biomarker perspective

People with type 2 diabetes are at an increased risk of cognitive impairment and dementia (including Alzheimer's disease), as well as subtle forms of cognitive dysfunction. Current diabetes guidelines recommend screening for cognitive impairment in groups at high risk and providing guidance for diabetes management in patients with diabetes and cognitive impairment. Yet, no disease-modifying treatment is available and important questions remain about the mechanisms underlying diabetes-associated cognitive dysfunction. These mechanisms are likely to be multifactorial and different for subtle and more severe forms of diabetes-associated cognitive dysfunction. Over the past years, research on dementia, brain ageing, diabetes, and vascular disease has identified novel biomarkers of specific dementia aetiologies, brain parenchymal injury, and cerebral blood flow and metabolism. These markers shed light on the processes underlying diabetes-associated cognitive dysfunction, have clear applications in current research and increasingly in clinical diagnosis, and might ultimately guide targeted treatment.

Screening for diabetic retinopathy: new perspectives and challenges

Although the prevalence of all stages of diabetic retinopathy has been declining since 1980 in populations with improved diabetes control, the crude prevalence of visual impairment and blindness caused by diabetic retinopathy worldwide increased between 1990 and 2015, largely because of the increasing prevalence of type 2 diabetes, particularly in low-income and middle-income countries. Screening for diabetic retinopathy is essential to detect referable cases that need timely full ophthalmic examination and treatment to avoid permanent visual loss. In the past few years, personalised screening intervals that take into account several risk factors have been proposed, with good cost-effectiveness ratios. However, resources for nationwide screening programmes are scarce in many countries. New technologies, such as scanning confocal ophthalmology with ultrawide field imaging and handheld mobile devices, teleophthalmology for remote grading, and artificial intelligence for automated detection and classification of diabetic retinopathy, are changing screening strategies and improving cost-effectiveness. Additionally, emerging evidence suggests that retinal imaging could be useful for identifying individuals at risk of cardiovascular disease or cognitive impairment, which could expand the role of diabetic retinopathy screening beyond the prevention of sight-threatening disease.

Retinal Microperimetry: A Useful Tool for Detecting Insulin Resistance-Related Cognitive Impairment in Morbid Obesity

Background: There is clear association between type 2 diabetes (T2D) and cognitive decline. Retinal microperimetry is a useful tool for detecting cognitive impairment in T2D. Morbid obesity (MO) has been associated with cognitive impairment. Insulin resistance (IR) seems a major determinant, but the data are unclear. The aim of this study was to evaluate the cognitive impairment in MO as well as the utility of retinal microperimetry in identifying these alterations. Methods: In total, 50 consecutive patients with MO were matched by age and gender with 30 healthy controls. All patients underwent cognitive evaluation (Montreal Cognitive Assessment Test-MoCA) and retinal microperimetry, using MAIA microperimeter 3rd generation. Retinal sensitivity and gaze fixation parameters were used for the evaluation of the analysis. Results: MO patients showed a significantly lower neurocognitive performance than the controls: MoCA score 24.94 ± 2.74 vs. 28.95 ± 1.05, p < 0.001. Cognitive function inversely correlated with the HOMA-IR (r = −0.402, p = 0.007). The AUROC for cognitive impairment using microperimetry was 0.807, CI 95% (0.592–0.947), p = 0.017. Conclusions: (1) Systemic insulin resistance is a major underlying mechanism accounting for the higher prevalence of cognitive impairment detected in young MO subjects. (2) Retinal microperimetry is a useful tool for identifying MO patients with cognitive impairment.