Early detection of Parkinson's disease: Retinal functional impairments as potential biomarkers
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease is typically diagnosed after substantial neurodegeneration despite early non-motor symptoms manifesting decades earlier. These changes offer a promising avenue for diagnostic exploration, especially within the eye, which has been proposed as a "window to the brain." OBJECTIVE: The aim was to identify biomarkers by validating the use of electroretinography, a non-invasive technique, to detect early retinal function anomalies reflecting central dysfunction. METHODS: Homozygous M83 transgenic mice (n = 10 males, 11 females), overexpressing human A53T α-synuclein, underwent behavioral tests and electroretinography measurements. Histological evaluation was performed at four months to analyze synucleinopathies and neurodegeneration. Electroretinography was also conducted with idiopathic PD patients (mean age 63.35 ± 7.73; disease duration 4.15 ± 2.06; H&Y score 2.07 ± 0.59; n = 12 males, 8 females) and healthy age-matched controls (mean age 61.65 ± 8.39; n = 9 males, 11 females). RESULTS: Rodent electroretinography revealed reduced photopic b-wave, PhNR b-wave, and PhNR-wave amplitudes at two and four months, particularly in females, indicating bipolar and retinal ganglion cell impairment. Based on retinal histological assessment, these changes might arise from α-synuclein pathology occurring in outer retinal layers. Likewise, the scotopic b-wave and PhNR waveform were similarly impaired in female participants with Parkinson's disease. The scotopic oscillatory potentials isolated further identified an attenuated amacrine cell output in females. CONCLUSIONS: Findings from both mice and human cohorts indicate that retinal functional impairments can be detected early in the progression of Parkinson's disease, particularly among females. These tools show promise in facilitating early diagnosis, disease monitoring, therapeutic intervention, and ultimately enhancing patient outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".