Impact of Shiftwork on Retinal Vasculature Diameters over a 5-Year Period: A Preliminary Investigation Using the BCOPS Study Data.

Our aim was to investigate the impact of shiftwork on changes in central retinal arteriolar equivalent (CRAE), a measure of arteriolar width, and central retinal venular equivalent (CRVE), a measure of venular width, over five years. The participants were 117 officers (72.7% men) examined at the first (2011-2014) and second (2015-2019) follow-up examinations in the Buffalo Cardio-Metabolic Occupational Police Stress study. Shiftwork data were obtained from the City of Buffalo, NY payroll records. Retinal diameters were measured using a standardized protocol. ANCOVA was used to compare mean change in CRAE and CRVE between the two examinations across shiftwork categories. Among men only, those who worked ≥70% hours on day shifts had a larger decrease in mean CRAE (-7.13 µm ± 2.51) compared to those who worked <70% day (-0.08 ± 0.96; p = 0.011). Among patrol officers, those who worked ≥70% day had a larger decrease in CRAE compared to those who worked <70% day (p = 0.015). Also, officers who worked ≥70% day had an increase in mean CRVE (µm) (4.56 ± 2.56) compared to those who worked <70% (-2.32 ± 1.32; p = 0.027). Over the five-year period, we observed adverse changes in arteriolar and venular diameters among officers who worked ≥70% on day shifts. The results should be interpreted with caution due to the small sample sizes.

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina.

Purpose: This study explored the relationship among microvascular parameters as delineated by optical coherence tomography angiography (OCTA) and retinal perfusion. Here, we introduce a versatile framework to examine the interplay between the retinal vascular structure and function by generating virtual vasculatures from central retinal vessels to macular capillaries. Also, we have developed a hemodynamics model that evaluates the associations between vascular morphology and retinal perfusion. Methods: The generation of the vasculature is based on the distribution of four clinical parameters pertaining to the dimension and blood pressure of the central retinal vessels, constructive constrained optimization, and Voronoi diagrams. Arterial and venous trees are generated in the temporal retina and connected through three layers of capillaries at different depths in the macula. The correlations between total retinal blood flow and macular flow fraction and vascular morphology are derived as Spearman rank coefficients, and uncertainty from input parameters is quantified. Results: A virtual cohort of 200 healthy vasculatures was generated. Means and standard deviations for retinal blood flow and macular flow fraction were 20.80 ± 7.86 µL/min and 15.04% ± 5.42%, respectively. Retinal blood flow was correlated with vessel area density, vessel diameter index, fractal dimension, and vessel caliber index. The macular flow fraction was not correlated with any morphological metrics. Conclusions: The proposed framework is able to reproduce vascular networks in the macula that are morphologically and functionally similar to real vasculature. The framework provides quantitative insights into how macular perfusion can be affected by changes in vascular morphology delineated on OCTA.

Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.

Eye diseases such as diabetic retinopathy are progressive with various changes in the retinal vessels, and it is difficult to analyze the disease for future treatment. There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.

Macroscopic and microscopic comparison of pecten oculi in different avian species.

The current study aims to present differences between the pecten oculi of different avian species through morphologic, macroscopic, light, and electron microscopic examinations. The study is a comprehensive research on seven avian species (sparrowhawk, hawk, magpie, swan, heron, pheasant, duck). The right eyes of the animals utilized in the study were removed for light microscopic examination, whereas their left eyes were removed for electron microscopic and macroscopic examinations. Morphometric analyses, as well as stereo and light microscopic measurements, were carried out on the pecten oculi of the animals. Given all these data, it was determined that the height of the pecten oculi did not differ among the species in the study; however, the pecten oculi were larger in birds with the highest value compared to the other species in the macroscopic measurements. Also, the pecten oculi vessels were larger, and the number of melanocytes was higher in keen eyesight, raptor, and migratory birds with large bulbus oculi. All these data suggest that the pecten oculi not only supplies nutrient to the retina but also contributes to sharp vision during migration and hunting, UV absorption from sunlight, as well as preservation of intraocular equilibrium.

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation.

Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions. However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive. Artificial intelligence (AI) has demonstrated great promise in retinal vessel segmentation. The development and evaluation of AI-based models require large numbers of annotated retinal images. However, the public datasets that are usable for this task are scarce. In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset. The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation. The annotation process was standardized through crowdsourcing among medical experts. The quality of each image was also evaluated. To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be beneficial to the further development of retinal vessel segmentation.

Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net.

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.

Retinal blood vessel calibre and vascular ageing in a general Spanish population: A EVA study.

INTRODUCTION: The aim of this work was to analyse the association of the retinal arteriolar calibre and the arteriole/venule index (AV index) with vascular ageing in a general population without previous cardiovascular disease. MATERIALS AND METHODS: Descriptive cross-sectional study. A total of 482 individuals without cardiovascular disease (mean age: 55.6 ± 14.2 years) were selected by random sampling, stratified by age and sex. The retinal arteriolar calibre was measured using digital fundus images of the back of the eye captured with a validated, semiautomatized and computer-assisted software (Index calculator). Vascular ageing was defined using three criteria based on the values of: (1) Carotid-femoral Pulse Wave Velocity (cfPWV), (2) Brachial-ankle Pulse Wave Velocity (baPWV) and (3) Carotid Intima-Media Thickness. RESULTS: The AV index and arteriolar calibre show a negative correlation with age, arterial pressure, cardiovascular risk and parameters of vascular structure and function (p < 0.001 in all cases). We found lower mean values of the AV index and arteriolar calibre in the individuals with early vascular ageing compared to those with healthy vascular ageing. AV index was negatively correlated with cfPWV ((ß=-2.9; 95% CI (-4.7; -1.1)), baPWV ((ß=-3.2; 95% CI (-5.4; -0.9)) and vascular ageing index ((ß=-1.7; 95% CI (-2.7; -0.7)). Arteriolar calibre showed a negative correlation with baPWV (ß=-0.1; 95% CI (-0.2; -0.1)). In the logistic regression analysis, lower values of AV index ((OR=0.01; 95% CI (0.01-0.10), OR=0.03; 95% CI (0.01-0.11) and OR=0.09; 95% CI (0.01-0.67)) were associated with EVA defined with cfPWV, baPWV and vascular ageing index respectively, and lower values of arteriolar calibre ((OR=0.71; 95% CI (0.55-0.91)) were associated with EVA defined with vascular ageing index. CONCLUSIONS: Lower values of AV index and retinal arteriolar calibre were associated with vascular ageing in a general Spanish population without previous cardiovascular disease.

Distance between the center of the FAZ measured automatically and the highest foveal bulge using OCT-angiography in elderly healthy eyes.

The center of the fovea, termed the foveola, is the area of highest visual acuity, has the highest density of cone photoreceptors. We investigated the distance between the automatically-determined center of the foveal avascular zone (FAZ) and the manually-determined highest foveal bulge (FB) point using single swept-source optical coherence tomography angiography (OCTA) instrument. This cross-sectional study included 49 eyes of 49 individuals (34 women and 15 men; median age: 68 years) with no history of ocular disorders. The FAZ in the superficial capillary plexus was automatically determined using the Kanno-Saitama macro method, and the center of the FAZ was automatically determined using ellipse approximation. Another candidate foveal center, the highest FB point, was determined manually on the serial cross-sectional B-scan images. As a result, the foveal center was manually identified as the highest FB point on B-scan OCTA images. The center of the FAZ was more likely to be located inferior to the highest FB point (p = 0.031). In participants with a total (linear) distance of more than 50 µm between the center of the FAZ and the highest FB point, the displacement was significantly more in the horizontal direction than in the vertical direction (p = 0.017). These results can be applicable to further studies regarding the spatial relationships between the center of the FAZ and the highest FB point in various macular diseases or previously-treated eyes.

Evaluation of flow of chorioretinal capillaries in healthy black and white subjects using optical coherence tomography angiography.

This study compared macular capillary parameters between healthy black and white subjects using optical coherence tomography angiography (OCTA). We measured vessel density (VD) of superficial (SCP), intermediate (ICP), and deep (DCP) capillary plexuses and choriocapillaris blood flow area (BFA) of the fovea, parafovea and total 3 mm-diameter circular area centered on the fovea, as well as the foveal avascular zone (FAZ) parameters, controlling for axial length. Black subjects had lower foveal and parafoveal VD in the SCP (p = 0.043 and p = 0.014) and the ICP (p = 0.014 and p = 0.002). In the DCP, black subjects had a trend toward lower foveal and parafoveal VD. Black subjects had decreased choriocapillaris BFA in the total 3 mm area (p = 0.011) and the parafovea (p = 0.033), larger FAZ area (p = 0.006) and perimeter (p = 0.014), and a higher capillary density in a 300 µm wide region around the FAZ (FD-300) (p = 0.001). There was no significant difference in FAZ acircularity index. To our knowledge, this is the first report analyzing the three distinct retinal capillary plexuses and identifying differing baseline VD, choriocapillaris and FAZ parameters in healthy young black compared to white subjects. Larger studies are needed to validate these findings and better understand racial differences in vulnerability to ocular diseases.

Normative intercapillary distance and vessel density data in the temporal retina assessed by wide-field spectral-domain optical coherence tomography angiography.

A limitation of conventional optical coherence tomography angiography (OCTA) is the limited field of view normally used in data acquisition. As the technology improves, larger fields of view that capture information away from the macular are being explored in order to provide an enhanced ability to detect pathology. However, normative measurements for important OCTA metrics like vessel density and intercapillary distance are not currently well-characterized in the peripheral retina. In this prospective study, we measured vessel density and intercapillary distance of the superficial vascular complex, ganglion cell layer plexus, and deep capillary plexus in montaged macular/temporal scans from 53 (33 men) healthy volunteers. Vessel density and intercapillary distance were also compared across different regions of the retina, including along arcs at separate distance from the fovea. Compared to the central macular region, the temporal retina had significantly lower vessel density, decreased thickness, and greater intercapillary distance in the superficial vascular complex, GCLP ganglion cell layer plexus, and deep capillary plexus (Wilcoxon rank sum test P < 0.001), with each of the plexuses examined here showing a general decrease in vessel density and an increase in intercapillary distance towards the temporal region. No significant difference was noted comparing corresponding vessel density and intercapillary distance regions above and below the macula, and multiple linear regression showed that age and intraocular pressure were not associated with vessel density and intercapillary distance in most models. Repeatability analysis reported as intraclass correlation coefficients demonstrated moderate to excellent reliability of vessel density and intercapillary distance in all OCTA layers. These results should help provide an enhanced baseline to help identify vascular pathology in the peripheral retina.

SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image.

METHODS: A new SERR-U-Net framework for retinal vessel segmentation is proposed, which leverages technologies including Squeeze-and-Excitation (SE), residual module, and recurrent block. First, the convolution layers of encoder and decoder are modified on the basis of U-Net, and the recurrent block is used to increase the network depth. Second, the residual module is utilized to alleviate the vanishing gradient problem. Finally, to derive more specific vascular features, we employed the SE structure to introduce attention mechanism into the U-shaped network. In addition, enhanced super-resolution generative adversarial networks (ESRGANs) are also deployed to remove the noise of retinal image. RESULTS: The effectiveness of this method was tested on two public datasets, DRIVE and STARE. In the experiment of DRIVE dataset, the accuracy and AUC (area under the curve) of our method were 0.9552 and 0.9784, respectively, and for SATRE dataset, 0.9796 and 0.9859 were achieved, respectively, which proved a high accuracy and promising prospect on clinical assistance. CONCLUSION: An improved U-Net network combining SE, ResNet, and recurrent technologies is developed for automatic vessel segmentation from retinal image. This new model enables an improvement on the accuracy compared to learning-based methods, and its robustness in circumvent challenging cases such as small blood vessels and intersection of vessels is also well demonstrated and validated.

MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.

Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the "layer-by-layer" information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology.

An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenvector direction angle of the Hessian matrix into the traditional Frangi filtering algorithm to enhance blood vessels of the global enhanced image. Next, noise interferences surrounding small blood vessels were eliminated through the improved mathematical morphological method. Then, blood vessels were segmented using the Otsu threshold method. The improved algorithm was tested by the public DRIVE and STARE data sets. According to the test results, the average segmentation accuracy, sensitivity, and specificity of retinal images in DRIVE and STARE are 95.54%, 69.42%, and 98.02% and 94.92%, 70.19%, and 97.71%, respectively. The improved algorithm achieved high average segmentation accuracy and low complexity while promising segmentation sensitivity. This improved algorithm can segment retinal vessels more accurately than other algorithms.

Air pollution and retinal vessel diameter and blood pressure in school-aged children in a region impacted by residential biomass burning.

Little is known about the early-life cardiovascular health impacts of fine particulate air pollution (PM2.5) and oxidant gases. A repeated-measures panel study was used to evaluate associations between outdoor PM2.5 and the combined oxidant capacity of O3 and NO2 (using a redox-weighted average, Ox) and retinal vessel diameter and blood pressure in children living in a region impacted by residential biomass burning. A median of 6 retinal vessel and blood pressure measurements were collected from 64 children (ages 4-12 years), for a total of 344 retinal measurements and 432 blood pressure measurements. Linear mixed-effect models were used to estimate associations between PM2.5 or Ox (same-day, 3-day, 7-day, and 21-day means) and retinal vessel diameter and blood pressure. Interactions between PM2.5 and Ox were also examined. Ox was inversely associated with retinal arteriolar diameter; the strongest association was observed for 7-day mean exposures, where each 10 ppb increase in Ox was associated with a 2.63 µm (95% CI - 4.63, - 0.63) decrease in arteriolar diameter. Moreover, Ox modified associations between PM2.5 and arteriolar diameter, with weak inverse associations observed between PM2.5 and arteriolar diameter only at higher concentrations of Ox. Our results suggest that outdoor air pollution impacts the retinal microvasculature of children and interactions between PM2.5 and Ox may play an important role in determining the magnitude and direction of these associations.

Numerical evaluation reveals the effect of branching morphology on vessel transport properties during angiogenesis.

Blood flow governs transport of oxygen and nutrients into tissues. Hypoxic tissues secrete VEGFs to promote angiogenesis during development and in tissue homeostasis. In contrast, tumors enhance pathologic angiogenesis during growth and metastasis, suggesting suppression of tumor angiogenesis could limit tumor growth. In line with these observations, various factors have been identified to control vessel formation in the last decades. However, their impacts on the vascular transport properties of oxygen remain elusive. Here, we take a computational approach to examine the effects of vascular branching on blood flow in the growing vasculature. First of all, we reconstruct a 3D vascular model from the 2D confocal images of the growing vasculature at postnatal day 5 (P5) mouse retina, then simulate blood flow in the vasculatures, which are obtained from the gene targeting mouse models causing hypo- or hyper-branching vascular formation. Interestingly, hyper-branching morphology attenuates effective blood flow at the angiogenic front, likely promoting tissue hypoxia. In contrast, vascular hypo-branching enhances blood supply at the angiogenic front of the growing vasculature. Oxygen supply by newly formed blood vessels improves local hypoxia and decreases VEGF expression at the angiogenic front during angiogenesis. Consistent with the simulation results indicating improved blood flow in the hypo-branching vasculature, VEGF expression around the angiogenic front is reduced in those mouse retinas. Conversely, VEGF expression is enhanced in the angiogenic front of hyper-branching vasculature. Our results indicate the importance of detailed flow analysis in evaluating the vascular transport properties of branching morphology of the blood vessels.

Comparative study of optical coherence tomography angiography algorithms for rodent retinal imaging.

Optical coherence tomography angiography (OCTA) is a functional extension of optical coherence tomography for non-invasive in vivo three-dimensional imaging of the microvasculature of biological tissues. Several algorithms have been developed to construct OCTA images from the measured optical coherence tomography signals. In this study, we compared the performance of three OCTA algorithms that are based on the variance of phase, amplitude, and the complex representations of the optical coherence tomography signals for rodent retinal imaging, namely the phase variance, improved speckle contrast, and optical microangiography. The performance of the different algorithms was evaluated by comparing the quality of the OCTA images regarding how well the vasculature network can be resolved. Quantities that are widely used in ophthalmic studies including blood vessel density, vessel diameter index, vessel perimeter index, vessel complexity index were also compared. Results showed that both the improved speckle contrast and optical microangiography algorithms are more robust than phase variance, and they can reveal similar vasculature features while there are statistical differences in the calculated quantities.

SA-Net: A scale-attention network for medical image segmentation.

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.

Investigation of associations between retinal microvascular parameters and albuminuria in UK Biobank: a cross-sectional case-control study.

BACKGROUND: Associations between microvascular variation and chronic kidney disease (CKD) have been reported previously. Non-invasive retinal fundus imaging enables evaluation of the microvascular network and may offer insight to systemic risk associated with CKD. METHODS: Retinal microvascular parameters (fractal dimension [FD] - a measure of the complexity of the vascular network, tortuosity, and retinal arteriolar and venular calibre) were quantified from macula-centred fundus images using the Vessel Assessment and Measurement Platform for Images of the REtina (VAMPIRE) version 3.1 (VAMPIRE group, Universities of Dundee and Edinburgh, Scotland) and assessed for associations with renal damage in a case-control study nested within the multi-centre UK Biobank cohort study. Participants were designated cases or controls based on urinary albumin to creatinine ratio (ACR) thresholds. Participants with ACR ≥ 3 mg/mmol (ACR stages A2-A3) were characterised as cases, and those with an ACR < 3 mg/mmol (ACR stage A1) were categorised as controls. Participants were matched on age, sex and ethnic background. RESULTS: Lower FD (less extensive microvascular branching) was associated with a small increase in odds of albuminuria independent of blood pressure, diabetes and other potential confounding variables (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.03-1.34 for arterioles and OR 1.24, CI 1.05-1.47 for venules). Measures of tortuosity or retinal arteriolar and venular calibre were not significantly associated with ACR. CONCLUSIONS: This study supports previously reported associations between retinal microvascular FD and other metabolic disturbances affecting the systemic vasculature. The association between retinal microvascular FD and albuminuria, independent of diabetes and blood pressure, may represent a useful indicator of systemic vascular damage associated with albuminuria.

Changes in physical activity behavior and development of cardiovascular risk in children.

The study aimed to investigate the association of changes in physical activity, screen time, and cardiorespiratory fitness (CRF) with development of body mass index (BMI), blood pressure (BP), and retinal microvascular health in children over four years. In 2014, 391 children aged 6-8 years were screened, and thereof 262 children were reexamined after four years following standardized protocols. Retinal arteriolar (CRAE) and venular diameters were measured by a retinal vessel analyzer. CRF was objectively assessed by a 20 m shuttle run, physical activity, and screen time by use of a questionnaire. Children who achieved higher CRF levels reduced their BMI (ß [95% CI] -0.35 [-0.46 to -0.25] kg/m2 per stage, P ≤ .001) and thereby developed wider CRAE (ß [95% CI] 0.25 [0.24 to 0.48] µm per stage, P = .03) at follow-up. Moreover, children with elevated or high systolic BP at baseline, but lower levels of screen time during the observation period, had wider CRAE at follow-up (ß [95% CI] -0.37 [-0.66 to -0.08] µm per 10 min/d, P = .013). Change in CRF was not directly associated with better microvascular health at follow-up. However, an increase of CRF over four years was associated with a reduced BMI and consequently wider retinal arterioles at follow-up. In children with elevated or high systolic BP, a reduction of screen time significantly improved retinal microvascular health as a primary prevention strategy to promote childhood health and combat development of manifest CV disease later in life.