ArticleScientific reports2016
Retinal Information is Independently Associated with Cardiovascular Disease in Patients with Type 2 diabetes.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05801575 (Efficacy of the CHM Teabag in Decreasing Stroke Risk Among Elderly People in Hong Kong), which is not on this map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Efficacy of the CHM Teabag in Decreasing Stroke Risk Among Elderly People in Hong Kong: A Stepped Wedge Cluster Randomized Trial
Who cites it
16 citing papers in PubMed.
- Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives.Journal of personalized medicine · 2023Review
- Association of retinopathy with risk of all-cause and specific-cause mortality in the National Health and Nutrition Examination Survey, 2005 to 2008.Frontiers in public health · 2023Article
- Article
- Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis.BMJ open diabetes research & care · 2022Article
- Assessment of image quality on color fundus retinal images using the automatic retinal image analysis.Scientific reports · 2022Article
- Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.Journal of clinical medicine · 2022Article
- Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes.Diabetologia · 2021Article
- Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images.Brain communications · 2021Article
- A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder.EClinicalMedicine · 2020Article
- Structural analysis of retinal blood vessels in patients with COPD during a pulmonary rehabilitation program.Scientific reports · 2020Observational
- Retinal image analytics detects white matter hyperintensities in healthy adults.Annals of clinical and translational neurology · 2019Article
- Age-related changes of the retinal microvasculature.PloS one · 2019Article
- Corydalis edulis Maxim. Promotes Insulin Secretion via the Activation of Protein Kinase Cs (PKCs) in Mice and Pancreatic β Cells.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the association between a series of retinal information and cardiovascular disease (CVD) and to evaluate whether this association is independent of traditional CVD risk factors in type 2 diabetes patients, we undertook an age-sex matched case-control study with 79 CVD cases and 150 non-CVD controls. All the participants underwent standardized physical examinations and retinal imaging. Retinal information was extracted from the retinal images using a semi-automatic computer program. Three stepwise logistic regression models were evaluated: model 1 with cardiovascular risk factors only; model 2 with retinal information only and model 3 with both cardiovascular risk factors and retinal information. The areas under the receiver operating characteristic curves (AUCs) were used to compare the performances of different models. Results showed that the AUCs were 0.692 (95%CI: 0.622-0.761) and 0.661 (95%CI: 0.588-0.735) for model 1 and model 2, respectively. In addition, model 3 had an AUC of 0.775 (95%CI: 0.716-0.834). Compared to the previous two models, the AUC of model 3 increased significantly (p < 0.05 in both comparisons). In conclusion, retinal information is independently associated with CVD in type 2 diabetes. Further work is needed to validate the translational value of applying retinal imaging analysis into clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.