Evidence map›Paper›PMID 42232963›Full record

ArticleFrontiers in medicine2026

Inner retinal layer thickness reflects plasma biomarkers in preclinical Alzheimer's disease.

Jane W Chan, Ziyuan Wang, Emily Xu, Ibrahim Abboud, Aya Alhasany, Sophia Xu, Xiaomeng Wu, Natalie Astraea, Fei Jiang, Zhihong Jewel Hu

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. 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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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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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

0 citing papers in PubMed.

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4 · The record

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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

10 authors.

Jane W ChanDepartment of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
Ziyuan WangDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.
Emily XuDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.
Ibrahim AbboudDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.
Aya AlhasanyDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.
Sophia XuDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.
Xiaomeng WuAnalytical Biochemistry Core Laboratory, Huntington Medical Research Institute, Pasadena, CA, United States.
Natalie AstraeaAnalytical Biochemistry Core Laboratory, Huntington Medical Research Institute, Pasadena, CA, United States.
Fei JiangDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.
Zhihong Jewel HuDoheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose/background: Few studies have examined layer- and region-specific retinal thickness in relation to plasma Alzheimer's disease (AD) biomarkers in cognitively normal adults at increased risk for AD. We conducted an exploratory pilot study to examine whether thickness measurements of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were associated with plasma AD biomarkers. Methods: Spectral-domain optical coherence tomography was performed on 20 eyes from 11 cognitively normal participants enriched for AD risk using a low plasma Aβ42/40 ratio (<0.10). Retinal layers were segmented using a novel deep-learning algorithm, followed by manual review and refinement by trained graders when needed. Associations between retinal thickness and plasma neurofilament-light chain (NfL), glial fibrillary acidic protein (GFAP), Aβ42/40, p-tau217, and p-tau181 were evaluated using ridge regression with participant-level bootstrap resampling. Results: Exploratory relationships suggest that thinner INL and GCL measurements are associated with higher p-tau217 and GFAP levels. Conclusion: Larger, longitudinal studies with confirmatory AD biomarker characterization are needed to validate these preliminary findings to determine how retinal OCT adds complementary value to plasma biomarkers for early AD risk assessment.

Indexed as

Alzheimer’s diseasedeep-learning segmentation algorithmplasma glial fibrillary acidic proteinplasma neurofilament-light chainplasma phosphorylated-tau217spectral-domain optical coherence tomography

Identifiers

PMID42232963
PMCPMC13222811

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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.