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Retinal Microvasculature in Health and Disease
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Retinal Microvasculature in Health and Disease

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiovascular Medicine".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 5994

Special Issue Editors

Prof. Dr. Nandu Goswami

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Guest Editor
1. Division of Physiology, Otto Loewi Research Center for Vascular Biology, Immunology and Inflammation, Medical University of Graz, Auenbruggerpl. 2, 8036 Graz, Austria
2. Center for Spaceflight and Aviation Health, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
Interests: cardiovascular system; heart rate variability; gravitational adaptation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Patrick De Boever

E-Mail Website
Guest Editor
1. Centre for Environmental Sciences, Hasselt University, 2610 Wilrijk, Belgium
2. Centre of Excellence Microbial Systems Technology, University of Antwerp, 2610 Wilrijk, Belgium
Interests: cardiovascular health; microvasculature; retina; urban health; epidemiology; air quality; lung microbiome; skin microbiome; probiotics; functional foods

Special Issue Information

Dear Colleagues,

The retina is a unique tissue that allows conveniently visualizing the smallest blood vessels. Fundus imaging and optical coherence tomography can capture structural blood vessel changes, while techniques such as dynamic vessel analysis characterize the functional status of the retinal microvasculature. Retinal metrics derived from these imaging techniques have been used in clinical and epidemiological studies and public health investigations. These metrics have the potential to identify the early effects of health risk factors (such as physical (in)activity, air pollution, dietary factors, etc.), map physiological changes (such as endothelial dysfunction, vascular reactivity, etc.), and differentiate between disease severities (diabetes, cardiovascular complications, etc.). This special issue welcomes submissions on state-of-the-art investigations that use retinal image analysis, including artificial intelligence, to measure the status of the microvasculature in health and disease.

Prof. Dr. Nandu Goswami
Prof. Dr. Patrick De Boever
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microvasculature
  • endothelial function
  • blood vessel analysis
  • urban health
  • cardiovascular risk
  • image analysis

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Published Papers (2 papers)

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Research

10 pages, 2062 KiB  
Article
The Association between Elevated Hematocrit and Retinal Artery Occlusion in Adult Patients
by Wei-Yu Lai, Pei-Chin Lin, Chun-Hao Yin, Kuang-Tsu Yang, En-Jie Shih and Jin-Shuen Chen
J. Clin. Med. 2022, 11(20), 6116; https://doi.org/10.3390/jcm11206116 - 17 Oct 2022
Viewed by 1866
Abstract
Retinal artery occlusion (RAO) is most commonly caused by embolism. Evidence showed that hematocrit (Hct) levels are often associated with embolic events. In this study, we aim to investigate the relationship between Hct levels and RAO. This retrospective study enrolled RAO patients between [...] Read more.
Retinal artery occlusion (RAO) is most commonly caused by embolism. Evidence showed that hematocrit (Hct) levels are often associated with embolic events. In this study, we aim to investigate the relationship between Hct levels and RAO. This retrospective study enrolled RAO patients between January 2011 and March 2020, who were 1:4 matched by age, gender, index date, and relevant comorbidities with the non-RAO group. Patient characteristics and laboratory data were collected. Univariate conditional logistic regression was applied by estimating crude matched odds ratios to determine the relevant factors for the occurrence of RAO. Furthermore, a narrative review of the relevant study was conducted to explore the association between Hct levels and embolism. Between January 2011 to March 2020, 82 RAO patients and 328 non-RAO patients matched with age, gender, index date, comorbidities of hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease, stroke, and atrial fibrillation were enrolled after excluding ineligible individuals. Conditional logistic regression analysis showed that Hct level ≥ 40% was associated with developing RAO. A forest plot showed a trend of a non-linear dose-response association between Hct levels and ischemic vascular events in male patients. Hct levels ≥ 40% in patients older than 65 years with at least six comorbidities could be associated with RAO. We suggest that older patients who have multiple comorbidities, combined with elevated Hct levels, should be informed of the possible occurrence of RAO. Full article
(This article belongs to the Special Issue Retinal Microvasculature in Health and Disease)
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11 pages, 1178 KiB  
Article
Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
by Yimin Qu, Jack Jock-Wai Lee, Yuanyuan Zhuo, Shukai Liu, Rebecca L. Thomas, David R. Owens and Benny Chung-Ying Zee
J. Clin. Med. 2022, 11(10), 2687; https://doi.org/10.3390/jcm11102687 - 10 May 2022
Cited by 4 | Viewed by 2904
Abstract
Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We [...] Read more.
Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders. Full article
(This article belongs to the Special Issue Retinal Microvasculature in Health and Disease)
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