Age‐related changes in the fractal dimension of the retinal microvasculature, effects of cardiovascular risk factors and smoking behaviour

Lemmens, Sophie; Luyts, Martial; Gerrits, Nele; Ivanova, Anna; Landtmeeters, Charlien; Peeters, Ron; Simons, Anne-Sophie; Vercauteren, Julie; Sunaric-Mégevand, Gordana; Keer, Karel Van; Molenberghs, Geert; Boever, Patrick De; Stalmans, Ingeborg

doi:10.1111/aos.15047

Cited by 8 publications

(7 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current smoking status was positively correlated with artery fractal dimension, vessel density, and artery and vein average width; was opposite in correlation to the correlation of blood pressure and sleep indices. Nevertheless, these results are consistent with a previous study 7 that reported a positive correlation between fractal dimension and the number of pack-years. A possible explanation is a pathological angiogenic effect, mediated by multiple growth factors, including vascular endothelial growth factor.…”

Section: Discussionsupporting

confidence: 93%

“…Other studies have utilized deep learning from fundus images, in order to directly predict mortality risk 4 and conditions such as hypertension, dyslipidemia, and obesity 3 . Some associations were also found in previous studies between fundus microvascular width, tortuosity, density or fractal dimension and obesity 5 , blood pressure 6 , smoking 7 , sleep apnea 8,9 , and blood glucose 3 .…”

Section: Introductionsupporting

confidence: 76%

See 1 more Smart Citation

Unveiling Associations Between Retinal Microvascular Architecture and Phenotypes Across Thousands of Healthy Subjects

Shapira,

Shilo,

Talmor-Barkan

et al. 2024

Preprint

View full text Add to dashboard Cite

Fundus imaging has emerged as a powerful tool, allowing for high-resolution and non-invasive visualization of the fundus microvasculature. Recent advancements in artificial intelligence (AI) have enabled the quantification of fundus microvascular characteristics, paving the way for the development of predictive markers for systemic conditions. In this study, we characterize the fundus microvasculature among 8467 healthy individuals, aged 40-70 years. We employ a fully automated segmentation tool to calculate 12 distinct vessel measures, including tortuosity density, vessel density, average width, fractal dimension, distance tortuosity, and curvature tortuosity for both arteries and veins. We provide reference values for these measures by age and sex, and explore the associations between these fundus vessel measures and various clinical features. Our analysis revealed numerous significant correlations, including connections between fundus arterial measures and cardiovascular measurements, lipid profile, sleep apnea indices, glycemic profile, body composition, and smoking status. This comprehensive analysis of fundus microvasculature anatomy in a large healthy population contributes to the growing field of oculomics and highlights the potential of fundus imaging for earlier detection and monitoring of systemic diseases such as metabolic syndrome, cardiovascular diseases and sleep disorders.

show abstract

Section: Discussionsupporting

confidence: 93%

Section: Introductionsupporting

confidence: 76%

Unveiling Associations Between Retinal Microvascular Architecture and Phenotypes Across Thousands of Healthy Subjects

Shapira,

Shilo,

Talmor-Barkan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…First, there are likely confounders for which we could not adjust because of a lack of data. For example, smoking is more prevalent among individuals with psychosis and is known to affect retinal vasculature . Second, our case definition of schizophrenia was based on ICD-10 codes from hospital admissions data, which may be prone to misclassification bias.…”

Section: Discussionmentioning

confidence: 99%

Association Between Retinal Features From Multimodal Imaging and Schizophrenia

et al. 2023

View full text Add to dashboard Cite

ImportanceThe potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts.ObjectiveTo investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population.Design, Setting, and ParticipantsThis cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022.Main Outcomes and MeasuresRetinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers.ResultsA total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (−4.05 μm, 95% CI, −5.40 to −2.69; P = 5.4 × 10−9), which persisted when investigating only patients without diabetes (−3.99 μm; 95% CI, −6.67 to −1.30; P = .004) or just those 55 years and younger (−2.90 μm; 95% CI, −5.55 to −0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (−0.14 units; 95% CI, −0.22 to −0.05; P = .001), although this was not present when excluding patients with diabetes.Conclusions and RelevanceIn this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

show abstract

“…[39][40][41] These features include arteriole narrowing, branching patterns, and vessel tortuosity. Kirin 44 To assure that age differences were not driving the results we found, we tested our model using both an age matched and non-age matched unrelated cohort. We found that performance improved slightly on the test set after age matching the related and unrelated cohorts.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks

Bhandari,

Singh,

Arun

et al. 2023

Preprint

View full text Add to dashboard Cite

Heritability of common eye diseases and ocular traits are relatively high. Here, we develop an automated algorithm to detect genetic relatedness from color fundus photographs (FPs). We estimated the degree of shared ancestry amongst individuals in the UK Biobank using KING software. A convolutional Siamese neural network-based algorithm was trained to output a measure of genetic relatedness using 7224 pairs (3612 related and 3612 unrelated) of FPs. The model achieved high performance for prediction of genetic relatedness; when computed Euclidean distances were used to determine probability of relatedness, the area under the receiver operating characteristic curve (AUROC) for identifying related FPs reached 0.926. We performed external validation of our model using FPs from the LIFE-Adult study and achieved an AUROC of 0.69. An occlusion map indicates that the optic nerve and its surrounding area may be the most predictive of genetic relatedness. We demonstrate that genetic relatedness can be captured from FP features. This approach may be used to uncover novel biomarkers for common ocular diseases.

show abstract

Age‐related changes in the fractal dimension of the retinal microvasculature, effects of cardiovascular risk factors and smoking behaviour

Cited by 8 publications

References 64 publications

Unveiling Associations Between Retinal Microvascular Architecture and Phenotypes Across Thousands of Healthy Subjects

Unveiling Associations Between Retinal Microvascular Architecture and Phenotypes Across Thousands of Healthy Subjects

Association Between Retinal Features From Multimodal Imaging and Schizophrenia

Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks

Contact Info

Product

Resources

About