Effects of the ecto-ATPase apyrase on microglial ramification and surveillance reflect cell depolarization, not ATP depletion.

Microglia, the brain's innate immune cells, have highly motile processes which constantly survey the brain to detect infection, remove dying cells, and prune synapses during brain development. ATP released by tissue damage is known to attract microglial processes, but it is controversial whether an ambient level of ATP is needed to promote constant microglial surveillance in the normal brain. Applying the ATPase apyrase, an enzyme which hydrolyzes ATP and ADP, reduces microglial process ramification and surveillance, suggesting that ambient ATP/ADP maintains microglial surveillance. However, attempting to raise the level of ATP/ADP by blocking the endogenous ecto-ATPase (termed NTPDase1/CD39), which also hydrolyzes ATP/ADP, does not affect the cells' ramification or surveillance, nor their membrane currents, which respond to even small rises of extracellular [ATP] or [ADP] with the activation of K+ channels. This indicates a lack of detectable ambient ATP/ADP and ecto-ATPase activity, contradicting the results with apyrase. We resolve this contradiction by demonstrating that contamination of commercially available apyrase by a high K+ concentration reduces ramification and surveillance by depolarizing microglia. Exposure to the same K+ concentration (without apyrase added) reduced ramification and surveillance as with apyrase. Dialysis of apyrase to remove K+ retained its ATP-hydrolyzing activity but abolished the microglial depolarization and decrease of ramification produced by the undialyzed enzyme. Thus, applying apyrase affects microglia by an action independent of ATP, and no ambient purinergic signaling is required to maintain microglial ramification and surveillance. These results also have implications for hundreds of prior studies that employed apyrase to hydrolyze ATP/ADP.

P2Y13 receptors regulate microglial morphology, surveillance, and resting levels of interleukin 1ß release.

Microglia sense their environment using an array of membrane receptors. While P2Y12 receptors are known to play a key role in targeting directed motility of microglial processes to sites of damage where ATP/ADP is released, little is known about the role of P2Y13 , which transcriptome data suggest is the second most expressed neurotransmitter receptor in microglia. We show that, in patch-clamp recordings in acute brain slices from mice lacking P2Y13 receptors, the THIK-1 K+ current density evoked by ADP activating P2Y12 receptors was increased by ~50%. This increase suggested that the P2Y12 -dependent chemotaxis response should be potentiated; however, the time needed for P2Y12 -mediated convergence of microglial processes onto an ADP-filled pipette or to a laser ablation was longer in the P2Y13 KO. Anatomical analysis showed that the density of microglia was unchanged, but that they were less ramified with a shorter process length in the P2Y13 KO. Thus, chemotactic processes had to grow further and so arrived later at the target, and brain surveillance was reduced by ~30% in the knock-out. Blocking P2Y12 receptors in brain slices from P2Y13 KO mice did not affect surveillance, demonstrating that tonic activation of these high-affinity receptors is not needed for surveillance. Strikingly, baseline interleukin-1ß release was increased fivefold while release evoked by LPS and ATP was not affected in the P2Y13 KO, and microglia in intact P2Y13 KO brains were not detectably activated. Thus, P2Y13 receptors play a role different from that of their close relative P2Y12 in regulating microglial morphology and function.

Potential retinal biomarkers for dementia: what is new?

PURPOSE OF REVIEW: To summarize the current findings on clinical retinal diseases and retinal imaging changes with dementia, focusing on Alzheimer's disease. RECENT FINDINGS: Studies observed that clinical retinal diseases such as age-related macular degeneration, open-angle glaucoma and diabetic retinopathy are related to dementia, but the associations are not entirely consistent. In terms of the retinal neuronal structure, multiple retinal neuronal layers are significantly thinner in Alzheimer's disease dementia, such as the parapapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GC-IPL). Recent studies further demonstrated that macular GC-IPL and macular RNFL are also significantly thinner in the preclinical stage of Alzheimer's disease. A thinner RNFL is also associated with a significantly increased risk of developing both cognitive decline and Alzheimer's disease dementia. In addition, studies consistently showed that retinal vascular changes are associated with poorer cognitive performance, as well as prevalent and incident Alzheimer's disease dementia. SUMMARY: The current findings support the concept that changes in the retina, particular in retinal neuronal structure and vasculature, can reflect the status of cerebral neuronal structure and vasculature, highlighting the potential role of retinal changes as biomarkers of dementia.

Spectral-Domain OCT Measurements in Alzheimer's Disease: A Systematic Review and Meta-analysis.

TOPIC: OCT is a noninvasive tool to measure specific retinal layers in the eye. The relationship of retinal spectral-domain (SD) OCT measurements with Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains unclear. Hence, we conducted a systematic review and meta-analysis to examine the SD OCT measurements in AD and MCI. CLINICAL RELEVANCE: Current methods of diagnosing early AD are expensive and invasive. Retinal measurements of SD OCT, which are noninvasive, technically simple, and inexpensive, are potential biomarkers of AD. METHODS: We conducted a literature search in PubMed and Excerpta Medica Database to identify studies published before December 31, 2017, that assessed the associations between AD, MCI, and measurements of SD OCT: ganglion cell-inner plexiform layer (GC-IPL), ganglion cell complex (GCC), macular volume, and choroidal thickness, in addition to retinal nerve fiber layer (RNFL) and macular thickness. We used a random-effects model to examine these relationships. We also conducted meta-regression and assessed heterogeneity, publication bias, and study quality. RESULTS: We identified 30 eligible studies, involving 1257 AD patients, 305 MCI patients, and 1460 controls, all of which were cross-sectional studies. In terms of the macular structure, AD patients showed significant differences in GC-IPL thickness (standardized mean difference [SMD], -0.46; 95% confidence interval [CI], -0.80 to -0.11; I2 = 71%), GCC thickness (SMD, -0.84; 95% CI, -1.10 to -0.57; I2 = 0%), macular volume (SMD, -0.58; 95% CI, -1.03 to -0.14; I2 = 80%), and macular thickness of all inner and outer sectors (SMD range, -0.52 to -0.74; all P < 0.001) when compared with controls. Peripapillary RNFL thickness (SMD, -0.67; 95% CI, -0.95 to -0.38; I2 = 89%) and choroidal thickness (SMD range, -0.88 to -1.03; all P < 0.001) also were thinner in AD patients. CONCLUSIONS: Our results confirmed the associations between retinal measurements of SD OCT and AD, highlighting the potential usefulness of SD OCT measurements as biomarkers of AD.

The cross-sectional and longitudinal relationship of diabetic retinopathy to cognitive impairment: a systematic review and meta-analysis.

OBJECTIVES: To establish a potential relationship between diabetic retinopathy (DR) and different stages of cognitive impairment METHODS: Literature searches were conducted on PubMed and EMBASE, with keywords "diabetic retinopathy" and "cognitive impairment". Inclusion criteria were original human studies, and English language. Quality of studies was assessed by the Newcastle-Ottawa Quality Assessment (NOSGEN). The register number of this study on the International Prospective Register of Systematic Reviews (PROSPERO) is CRD42021236747. The main outcome measures were odds ratios (OR) and risk ratios (RR) for cross-sectional and longitudinal studies, respectively. Meta-regression was performed to evaluate the effects of potential moderator variables, including, age, onset age of diabetes mellitus (DM), duration of DM, and HbA1c. RESULTS: Twenty-five studies (17 cross-sectional and 8 longitudinal studies) with a total of 1,963,914 subjects, were included. Among the cross-sectional studies, the pooled ORs of any cognitive impairment, early stage of cognitive impairment and dementia in subjects with DR (95% confidence interval) were 1.48 (1.08-2.02), 1.59 (1.01-2.51), and 1.13 (0.86-1.50), respectively. Among the longitudinal studies, the pooled RRs of any cognitive impairment, early stage of cognitive impairment, and dementia in subjects with DR (95% confidence interval) were 1.35 (1.12-1.65), 1.50 (1.06-2.12), and 1.31 (1.03-1.66), respectively. Meta-regression showed age, onset age of DM, duration of DM, and glycated hemoglobin (HbA1c) were not statistically associated with the outcomes. CONCLUSIONS: The presence of DR in DM patients indicates both higher odds of prevalent cognitive impairment and escalated risks of developing cognitive impairment in the future.

Alterations in the Choroidal Sublayers in Relationship to Severity and Progression of Diabetic Retinopathy: A Swept-Source OCT Study.

Purpose: To examine the association of baseline choroidal sublayers metrics with the risk of diabetic retinopathy (DR) progression over 2 years, with adjustment for confounding factors that affect choroidal measurements. Design: Prospective, observational cohort study. Participants: One hundred three eyes from 62 patients with diabetes mellitus (DM). Methods: Patients were followed up at 6-month intervals for at least 2 years. Choroidal metrics including choroidal area, choroidal thickness (CT), and choroidal vascularity index were measured for both (1) the choriocapillaris plus Sattler's layer and (2) the Haller's layer within the subfoveal and parafoveal region. Cox proportional models were constructed to estimate the relationship between baseline choroidal metrics and DR progression, adjusted for intereye correlation, established risk factors (i.e., duration of DM, glycated hemoglobin [HbA1c] level, body mass index [BMI], use of insulin, and mean arterial blood pressure [MABP]) and confounding factors of choroidal measurements (i.e., age and axial length). Additional predictive value of choroidal metrics was assessed using the C-statistic. Main Outcome Measures: Hazard ratios (HRs) calculated by Cox proportional hazards model to demonstrate the associations between baseline choroidal metrics and DR progression. Results: After adjusting for age, axial length, and intereye correlation, choroidal metrics in Haller's layer at baseline that were associated with a higher risk of DR progression included increases in subfoveal choroidal area (HR, 2.033; 95% confidence interval [CI], 1.179-3.505; P = 0.011), subfoveal plus parafoveal choroidal area (HR, 1.909; 95% CI, 1.096-3.326; P = 0.022), subfoveal CT (HR, 2.032; 95% CI, 1.181-3.498; P = 0.010), and subfoveal plus parafoveal CT (HR, 1.908; 95% CI, 1.097-3.319; P = 0.022). These associations remained statistically significant after additionally adjusting for duration of DM, HbA1c level, BMI, use of insulin, and MABP. Addition of these choroidal metrics significantly improved the discrimination for DR progression when compared with established risk factors alone (e.g., duration of DM and HbA1c; increase in C-statistic ranged from 8.08% to 9.67% [P < 0.05]). Conclusions: Eyes with a larger choroidal area and CT in Haller's layer at baseline were associated with a higher risk of DR progression over 2 years.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS: In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid ß burden (testing the deep learning model between amyloid ß positive vs amyloid ß negative). FINDINGS: 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid ß positive and those who were amyloid ß negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION: A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING: BrightFocus Foundation.

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods: A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results: For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions: Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance: The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

Assessment of retinal neurodegeneration with spectral-domain optical coherence tomography: a systematic review and meta-analysis.

OBJECTIVES: To comprehensively assess diabetic retinopathy neurodegeneration (DRN) as quantified by retinal neuronal and axonal layers measured with spectral-domain optical coherence tomography (SD-OCT) in subjects with diabetes mellitus (DM). METHODS: Articles on the topic of examining macular ganglion cell-inner plexiform layer (m-GCIPL), macular retinal nerve fibre layer (m-RNFL), macular ganglion cell complex (m-GCC), and peripapillary RNFL (p-RNFL) measured with SD-OCT in DM subjects without DR (NDR) or with non-proliferative DR (NPDR) were searched in PubMed and Embase up to November 31, 2019. Standardized mean difference (SMD) as effect size were pooled using random-effects model. RESULTS: Thirty-six studies searched from online databases and the CUHK DM cohort were included in the meta-analysis. In the comparison between NDR and control, macular measures including mean m-GCIPL (SMD = -0.26, p = 0.003), m-RNFL (SMD = -0.26, p = 0.046), and m-GCC (SMD = -0.28; p = 0.009) were significantly thinner in the NDR group. In the comparison between NPDR and NDR, only mean p-RNFL was significantly thinner in the NPDR group (SMD = -0.27; p = 0.03), but not other macular measures. CONCLUSIONS: Thinning of retinal neuronal and axonal layers at macula as measured by SD-OCT are presented in eyes with NDR, supporting DRN may be the early pathogenesis in the DM patients without the presence of clinical signs of DR. In the future, these SD-OCT measures may be used as surrogates of DRN to stratify DM patients with a high risk of DR, and may be used as a therapeutic target if neuroprotection treatment for DR is available.

Using Retinal Imaging to Study Dementia.

The retina offers a unique "window" to study pathophysiological processes of dementia in the brain, as it is an extension of the central nervous system (CNS) and shares prominent similarities with the brain in terms of embryological origin, anatomical features and physiological properties.  The vascular and neuronal structure in the retina can now be visualized easily and non-invasively using retinal imaging techniques, including fundus photography and optical coherence tomography (OCT), and quantified semi-automatically using computer-assisted analysis programs. Studying the associations between vascular and neuronal changes in the retina and dementia could improve our understanding of dementia and, potentially, aid in diagnosis and risk assessment.  This protocol aims to describe a method of quantifying and analyzing retinal vasculature and neuronal structure, which are potentially associated with dementia. This protocol also provides examples of retinal changes in subjects with dementia, and discusses technical issues and current limitations of retinal imaging.