Evidence map›Paper›PMID 41081150›Full record

ArticleQuantitative imaging in medicine and surgery2025

Relationships between multimodal ocular imaging and white matter hyperintensity volume.

Rui Li, Ying Hui, Jing Li, Changkai Ji, Changde Du, Hao Su, Shun Zhang, Bin Lv, Yuan Ni, Xiaoshuai Li and 7 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Rui Li *Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Ying Hui *Department of Medical Imaging, Kailuan General Hospital, Tangshan, China.
Jing Li *Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Changkai JiState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Changde DuState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Hao SuPing An Healthcare Technology, Beijing, China.
Shun ZhangDepartment of Psychiatry, Kailuan Mental Health Centre, Tangshan, China.
Bin LvPing An Healthcare Technology, Beijing, China.
Yuan NiPing An Healthcare Technology, Beijing, China.
Xiaoshuai LiDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Xiaoliang LiangDepartment of Psychiatry, Kailuan Mental Health Centre, Tangshan, China.
Hongyang LiDepartment of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Ling YangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Guotong XiePing An Healthcare Technology, Beijing, China.
Huiguang HeState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Jingli GaoDepartment of Intensive Care Unit, Kailuan General Hospital, Tangshan, China.
Zhenchang WangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: White matter hyperintensities (WMH), a key imaging marker of cerebral small vessel disease, are associated with stroke, cognitive decline, and mortality in aging populations, yet current magnetic resonance imaging (MRI)-based assessments are costly and unsuitable for large-scale monitoring. Owing to shared microvascular characteristics between the retina and brain, ocular imaging offers a non-invasive alternative, but most studies have examined single-modal retinal metrics or total WMH volume, with little attention to multimodal biomarkers and lobe-specific WMH. This study aims to explore the associations between multimodal ocular imaging biomarkers and WMH volume to enable earlier, non-invasive detection of cerebral small vessel disease. Methods: Participants (age: 23-83 years) in the KaiLuan Study underwent comprehensive ophthalmic and neuroimaging evaluations, including optical coherence tomography (OCT) for measuring macular retinal nerve fiber layer (mRNFL), macular ganglion cell-inner plexiform layer (mGCIPL), macular inner nuclear layer (mINL), macular ganglion cell complex (mGCC), and total macular thickness. Fundus photography was used to assess central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), arteriole/venular ratio (AVR), fractal dimension (FD), global vein width, and global artery width. MRI was employed to quantify WMH volume. Generalized linear regression models were used to analyze associations between ocular biomarkers and WMH, with adjustments for demographics and vascular risk factors. Results: The study included 761 participants (average age 55 years, 54.4% women). Thinner mGCIPL and mGCC were significantly associated with greater WMH volume in the total brain (P<0.02), temporal lobes (P≤0.02), and occipital lobes (P≤0.003). Reduced mINL correlated with larger WMH in the total brain and specific lobes (P<0.04). No significant associations were found for mRNFL or total macular thickness (all P>0.05). Smaller AVR, narrower CRAE, and wider CRVE were linked to higher WMH volume in the occipital lobe (P<0.02). Lower FD complexity and wider global vein width demonstrated significant associations with increased WMH volume in the total brain and individual lobes (frontal, occipital, and temporal lobes; all P<0.01). Sex-stratified analysis showed that these associations were stronger in females, with thinner mGCIPL, mINL, and wider global vein width associated with greater total and lobe-specific WMH (P<0.05). Conclusions: It is suggested that parameters from OCT and fundus photography are independently associated with WMH volume. These parameters may serve as potential biomarkers for the early detection and longitudinal monitoring of WMH, enabling precise identification and ongoing surveillance of white matter alterations.

Indexed as

fundus photographyoptical coherence tomography (OCT)retinal thicknessWhite matter hyperintensity volume (WMH volume)

Identifiers

PMID41081150
PMCPMC12514701

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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.