Evidence map›Paper›PMID 40052159›Full record

ArticleResearch (Washington, D.C.)2025

Retinal Vascular Morphology Reflects and Predicts Cerebral Small Vessel Disease: Evidences from Eye-Brain Imaging Analysis.

Ning Wu, Mingze Xu, Shuohua Chen, Shouling Wu, Jing Li, Ying Hui, Xiaoshuai Li, Zhenchang Wang, Han Lv

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ning WuDepartment of Medical Imaging, Yanjing Medical College, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-4446-0338
Mingze XuCenter for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Shuohua ChenDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Shouling WuDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Jing LiDepartment of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Ying HuiDepartment of Radiology, Kailuan General Hospital, Tangshan, China.
Xiaoshuai LiDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.
Zhenchang WangDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.
Han LvDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.ORCID https://orcid.org/0000-0001-9559-4777

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cerebral small vessel disease (SVD) involves ischemic white matter damage and choroid plexus (CP) dysfunction for cerebrospinal fluid (CSF) production. Given the vascular and CSF links between the eye and brain, this study explored whether retinal vascular morphology can indicate cerebrovascular injury and CP dysfunction in SVD. We assessed SVD burden using imaging phenotypes like white matter hyperintensities (WMH), perivascular spaces, lacunes, and microbleeds. Cerebrovascular injury was quantified by WMH volume and peak width of skeletonized mean diffusivity (PSMD), while CP volume measured its dysfunction. Retinal vascular markers were derived from fundus images, with associations analyzed using generalized linear models and Pearson correlations. Path analysis quantified contributions of cerebrovascular injury and CP volume to retinal changes. Support vector machine models were developed to predict SVD severity using retinal and demographic data. Among 815 participants, 578 underwent ocular imaging. Increased SVD burden markedly correlated with both cerebral and retinal biomarkers, with retinal alterations equally influenced by cerebrovascular damage and CP enlargement. Machine learning models showed robust predictive power for severe SVD burden (AUC was 0.82), PSMD (0.81), WMH volume (0.77), and CP volume (0.80). These findings suggest that retinal imaging could serve as a cost-effective, noninvasive tool for SVD screening based on vascular and CSF connections.

Identifiers

PMID40052159
PMCPMC11883085

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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.