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Record W4406033641 · doi:10.1002/trc2.70025

Association of corneal endothelial cell morphology with neurodegeneration in mild cognitive impairment and dementia

2025· article· en· W4406033641 on OpenAlexaff
Georgios Ponirakis, Hanadi Al Hamad, Alaa S. Al‐Waisy, Ioannis N. Petropoulos, Adnan Khan, Hoda Gad, Mani Chandran, Masharig Gadelseed, Salah Mahmoud, Ahmed Elsotouhy, Marwan Ramadan, Shafi Khan, Rüştü Emre Akcan, Priya V. Gawhale, Noushad Thodi, Tala Nakouzi, Moayad Homssi, Nebras H. Hadid, Aisha Al Obaidan, Rawan Hussein, Ahmed Own, Ashfaq Shuaib, Rayaz A. Malik

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Alberta
FundersWeill Cornell Medicine - QatarQatar National Research FundWeill Cornell Medical CollegeFonds National de la Recherche LuxembourgQatar Foundation
KeywordsNeurodegenerationCognitive impairmentDementiaAssociation (psychology)NeurosciencePsychologyMedicineCognitionPathologyPsychotherapistDisease

Abstract

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Abstract INTRODUCTION Corneal confocal microscopy (CCM) detects neurodegeneration in mild cognitive impairment (MCI) and dementia and identifies subjects with MCI who develop dementia. This study assessed whether abnormalities in corneal endothelial cell (CEC) morphology are related to corneal nerve morphology, brain volumetry, cerebral ischemia, and cognitive impairment in MCI and dementia. METHODS Participants with no cognitive impairment (NCI), MCI, and dementia underwent CCM to quantify corneal endothelial cell density (CECD) and area (CECA), corneal nerve fiber morphology, magnetic resonance imaging (MRI) brain volumetry, and severity of brain ischemia. RESULTS Of the 114 participants, 14 had NCI, 77 had MCI, and 23 had dementia. CECD (1971.3 ± 594.6 vs 2316.1 ± 499.5 cells/mm 2 , p < 0.05) was significantly lower in the dementia compared to the NCI group. CECD and CECA were comparable between the MCI and NCI groups ( p = 0.13–0.65). Corneal nerve fiber density (CNFD) (31.7 ± 5.6 vs 24.5 ± 9.2 and 17.3 ± 5.3 fibers/mm 2 , p < 0.01), corneal nerve branch density (CNBD) (111.8 ± 58.1 vs 50.4 ± 36.4 and 52.7 ± 21.3 branches/mm 2 , p < 0.0001), and corneal nerve fiber length (CNFL) (24.6 ± 6.6 vs 16.5 ± 6.8 and 16.2 ± 5.0 mm/mm 2 , p < 0.0001) were lower in the MCI and dementia groups compared to the NCI group. Lower CECD partially mediated the impact of age and diabetes on CNFL reduction ( p < 0.05), whereas CECA lost its significance after adjustment ( p = 0.20). CEC morphology does not affect the association between corneal nerve fiber loss and MCI/dementia. CECD and CECA had no significant association with cerebral ischemic lesions ( p = 0.21–0.47), dementia ( p = 0.11–0.35), or cognitive decline ( p = 0.37–0.38). However, lower CECD and higher CECA were associated with decreased cortical gray matter volume ( p < 0.05–0.01). DISCUSSION CEC loss occurs in patients with dementia, and both endothelial cell loss and hypertrophy are associated with cortical gray matter atrophy. CNF loss occurs in individuals with MCI and dementia. Corneal nerve and endothelial cell abnormalities could act as biomarkers for neurovascular pathology in dementia. Highlights Corneal endothelial cell density is significantly reduced in patients with dementia. Corneal nerve fiber density, branch density, and length are lower in subjects with mild cognitive impairment (MCI) and dementia. Corneal endothelial cell loss and hypertrophy are associated with cortical gray matter atrophy. Corneal nerve and endothelial cell abnormalities could act as biomarkers for neurovascular pathology in dementia. Reduced corneal endothelial cell density partially mediates the effects of age and diabetes on corneal nerve fiber loss.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.126
GPT teacher head0.440
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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