Associations of inner retinal layers with risk of incident dementia: An individual participant data analysis of four prospective cohort studies

INTRODUCTION: Our main objective was to investigate whether retinal neurodegeneration, estimated from lower thickness of inner retinal layers, was associated with incident all-cause dementia and Alzheimer's disease (AD). METHODS: We performed an individual participant data meta-analysis using unpublished data from four prospective cohort studies with a total of 69,955 participants (n = 1087 cases of incident all-cause dementia; n = 520 cases incident AD; follow-up time median [interquartile range] 11.3 [8.8-11.5] years). RESULTS: General baseline characteristics of the study population were mean (standard deviation) age, 58.1 (8.8) years; 47% women. After adjustment, lower baseline macular retinal nerve fiber layer thickness was significantly associated with a 10% and 11% higher incidence of all-cause dementia and AD, respectively. Lower baseline macular ganglion cell-inner plexiform layer thickness was not significantly associated with these outcomes. DISCUSSION: These findings suggest that retinal neurodegeneration precedes the onset of clinical dementia. Retinal imaging tools may be informative biomarkers for the study of the early pathophysiology of dementia

Overflow phenomenon in serum lutein after supplementation:a systematic review supported with SNPs analyses

Lutein, a type of carotenoids, is found to delay the onset and progression of age-related macular degeneration (AMD). Several lutein supplementation studies showed that after an initial increase, lutein serum levels demonstrated a subsequent decrease despite continuous supplementation. In this systematic literature review, this obscure phenomenon was tried to be explained. The subsequent drop in lutein levels was postulated due to down-regulation of lutein receptors scavenger receptor class B type I (SR-BI) in the gastrointestinal tract, upregulation of lutein degrading enzyme β-carotene dioxygenase (BCDO2), or perhaps a combination of both. Some single nucleotides polymorphisms (SNPs) that could have influence on the occurrence of this phenomenon. To date, an exact scientific explanation for this phenomenon has not been established. Further research is needed to investigate this phenomenon in depth to reach an irrefutable explanation, giving that lutein is proven to be effective in delaying the onset and progression of AMD and its metabolism in the human body becomes of equal importance

A systematic review and meta-analysis of Optical coherence tomography studies in Schizophrenia, Bipolar disorder and Major depressive disorder

Objectives- Due to the common neurodevelopmental origin and easy accessibility, the retina serves as a surrogate marker for changes in the brain. Hence, Optical Coherence Tomography (OCT), a tool to examine the neuronal layers of retina has gained importance in investigating psychiatric disorders. Several studies in the last decade have reported retinal structural alterations in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, the findings are inconsistent. Hence, we conducted a meta-analysis to investigate alterations in OCT parameters in patients with SCZ, BD and MDD

Artificial intelligence for detecting keratoconus

BACKGROUND: Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting.OBJECTIVES: To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022.SELECTION CRITERIA: We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists.DATA COLLECTION AND ANALYSIS: Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach.MAIN RESULTS: We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results.AUTHORS' CONCLUSIONS: AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.</p

A systematic review and meta-analysis of Optical coherence tomography studies in Schizophrenia, Bipolar disorder and Major depressive disorder

Objectives- Due to the common neurodevelopmental origin and easy accessibility, the retina serves as a surrogate marker for changes in the brain. Hence, Optical Coherence Tomography (OCT), a tool to examine the neuronal layers of retina has gained importance in investigating psychiatric disorders. Several studies in the last decade have reported retinal structural alterations in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, the findings are inconsistent. Hence, we conducted a meta-analysis to investigate alterations in OCT parameters in patients with SCZ, BD and MDD.</p

Association between retinal vascular measures and brain white matter lesions in schizophrenia

OBJECTIVE: Recent studies have examined retinal vascular abnormalities in schizophrenia as retinal vascular imaging is a non-invasive proxy to cerebral microvasculature. However, relation between retinal vascular abnormalities and brain structure is not well examined in schizophrenia. Hence in this study, for the first time, we examined the relationship between retinal vascular measures and brain white matter lesions in schizophrenia. We examined brain white matter lesions as they are considered a predictive marker for future adverse cerebrovascular event. METHODS: We acquired retinal vascular images of both eyes using a non-mydriatic camera and calculated retinal vascular diameter, tortuosity, trajectory and fractal dimension using validated methods. All patients underwent Magnetic Resonance Imaging of bran and we computed white matter hypo-intensities using Freesurfer software. We performed a linear regression analysis to examine the relationship between white matter hypo-intensities and retinal vascular measures controlling for age, sex, fasting blood sugar, creatinine, whole-brain volume, and antipsychotic dose. RESULTS: The regression model was significant in Schizophrenia patients (R=0.983;R2 =0.966;-F=10.849;p = 0.008) but not in healthy volunteers (R=0.828;R2 =0.686;F=0.182; p = 0.963). Among the retinal vascular measures, arterial tortuosity (β = 0.963;p-0.002), tortuosity (β = -1.002;p = 0.001) and fractal dimension (β = -0.688;p = 0.014) were significant predictors of white matter lesions. DISCUSSION: The current study's findings support the conclusion that retinal vascular fractal dimension and tortuosity are associated with changes in cerebral white matter and may be considered proxy markers for cerebral microvasculature in schizophrenia. Considering the relationship between white matter lesions and stroke, these observations could have important clinical implications to screen schizophrenia patients for risk of adverse cerebrovascular event

Association between retinal vascular measures and brain white matter lesions in schizophrenia

OBJECTIVE: Recent studies have examined retinal vascular abnormalities in schizophrenia as retinal vascular imaging is a non-invasive proxy to cerebral microvasculature. However, relation between retinal vascular abnormalities and brain structure is not well examined in schizophrenia. Hence in this study, for the first time, we examined the relationship between retinal vascular measures and brain white matter lesions in schizophrenia. We examined brain white matter lesions as they are considered a predictive marker for future adverse cerebrovascular event. METHODS: We acquired retinal vascular images of both eyes using a non-mydriatic camera and calculated retinal vascular diameter, tortuosity, trajectory and fractal dimension using validated methods. All patients underwent Magnetic Resonance Imaging of bran and we computed white matter hypo-intensities using Freesurfer software. We performed a linear regression analysis to examine the relationship between white matter hypo-intensities and retinal vascular measures controlling for age, sex, fasting blood sugar, creatinine, whole-brain volume, and antipsychotic dose. RESULTS: The regression model was significant in Schizophrenia patients (R=0.983;R2 =0.966;-F=10.849;p = 0.008) but not in healthy volunteers (R=0.828;R2 =0.686;F=0.182; p = 0.963). Among the retinal vascular measures, arterial tortuosity (β = 0.963;p-0.002), tortuosity (β = -1.002;p = 0.001) and fractal dimension (β = -0.688;p = 0.014) were significant predictors of white matter lesions. DISCUSSION: The current study's findings support the conclusion that retinal vascular fractal dimension and tortuosity are associated with changes in cerebral white matter and may be considered proxy markers for cerebral microvasculature in schizophrenia. Considering the relationship between white matter lesions and stroke, these observations could have important clinical implications to screen schizophrenia patients for risk of adverse cerebrovascular event