Corneal Nerve Fiber Loss Relates to Cognitive Impairment in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Abstract Background Cognitive impairment in Parkinson’s disease (PD) adversely influences quality of life. There is currently no available biomarker to predict cognitive decline in PD. PD involves both the central and peripheral nervous system and especially small fiber damage occurs in PD. Corneal confocal microscopy (CCM) has been used as a non-invasive tool for quantifying small nerve fibre damage in PD. The present study investigated whether corneal nerve measures were associated with cognitive function in PD. Methods Patients with PD were classified into those with normal cognitive function (PD-CN), mild cognitive impairment (PD-MCI), and dementia (PDD). Corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD) and corneal nerve fiber length (CNFL) were quantified with CCM and compared with a control group.Results Sixty-five PD patients (44.62% male; mean age 64.60±6.95 years; mean disease duration 4.63±2.53 years) and 30 controls (53.33% male; mean age 62.43±6.16 years) were studied. CNFD was decreased and CNBD was increased in PD patients compared to controls ( P <0.05). CNFD decreased progressively with decline in cognitive function in PD patients. CNBD and CNBD/CNFD ratio was higher in PD-CN compared to controls but decreased with worsening cognitive function in PD-MCI and PDD patients. CNFD correlated with the Montreal cognitive assessment (MoCA) score ( r =0.683, P <0.0001), unified Parkinson disease rating scale (UPDRS)-part III ( r =-0.481, P <0.0001) and total UPDRS scores ( r =-0.401, P <0.0001) in PD patients. CNFD, CNBD, CNFL were lower and CNBD/CNFD ratio was higher with increasing Hoehn and Yahr stage. There was no correlation between CNFD and Levodopa equivalent daily dose (LEDD) ( r =0.176, P =0.161). CNFD, CNBD and CNFL could discriminate between PD-MCI and PD-CN with an area under the curve (AUC) of 82.85%, 67.47%, and 78.74%, respectively. CNFD, CNBD and CNFL could discriminate between PDD and PD-CN with an AUC of 96.67%, 90.12% and 84.44%. A combination of all three CCM parameters further increased the AUC value. Conclusions PD patients show evidence of corneal nerve loss compared with controls and corneal nerve parameters are associated with the severity of cognitive and motor dysfunction in PD. CCM could serve as an objective in vivo ophthalmic imaging technique to assess neurodegeneration in PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".