A strong association between optical coherence tomography (OCT) retinal images  and Parkinson’s disease has been found, signaling a potentially lower-cost and less-invasive method of diagnosing the disease, according to researchers.

Parkinson’s disease is the second most common neurodegenerative disease that involves the loss of dopaminergic neurons. Symptoms of the disease include a range of motor and non-motor features. The non-motor symptoms of Parkinson’s disease include motion perception issues, reduced spatial contrast sensitivity, visual hallucinations, and color deficiency. These symptoms are often experienced during the early stages of the disease, sometimes even before the condition affects the brain, the study explains.

Previous studies have found that the dopamine dysfunction that occurs with Parkinson’s disease is seen both in the nigrostriatal complex and the retina. Retinal tissue has been considered “a window of the central nervous system,” because retinal pathological changes may precede or accompany deterioration in brain tissue, according to the authors of the study, which has been accepted for publication in the European Journal of Neurology. As such, there’s a push to find potential ocular biomarkers that could be used to detect Parkinson’s at an early stage, when therapy may help prevent the progression of the disease. While magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) can help detect the disease, these tests are both expensive and invasive, making them less than ideal widespread screening tools.


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To assess the usefulness of OCT measurements as potential imaging biomarkers for Parkinson’s disease, the researchers conducted a systematic review and meta-analysis of observational studies to compare patients with Parkinson’s disease against healthy controls. Only fully peer-reviewed articles with full text available in English were included. The team looked at peripapillary retinal nerve fiber layer (pRNFL) thickness, macular ganglion cell complex (mGCC) thickness, macular thickness, and macular volume. 

Investigators identified 36 observational studies that included 1712 patients with Parkinson’s disease (2548 eyes) and 1778 healthy controls (2646 eyes). The pooled results shows a significant reduction in the pRNFL thickness and mGCC thickness in patients with Parkinson’s disease compared with healthy controls. A greater reduction in mean pRNFL thickness was associated with a lower mean mGCC thickness, leading the team to conclude that thinner pRNFL and mGCC might indicate fewer retinal ganglion cells in patients with Parkinson’s disease. In addition, they observed “robust associations” between Parkinson’s disease and decreased macular thickness and volume, which may be due to a significant reduction in pRNFL and mGCC. 

Specifically, compared with the healthy group, the eyes of patients with Parkinson’s disease showed a significant reduction in:

Mean pRNFL thickness (weighted mean difference [WMD], ‐3.51 μm [95% confidence intervals (CI): ‐4.84, ‐2.18], P =.000)

  • Mean pRNFL thickness (weighted mean difference [WMD], ‐3.51 μm [95% confidence intervals (CI): ‐4.84, ‐2.18], P =.000)
  • All quadrants at pRNFL (WMD range, ‐7.65 ~ ‐2.44 μm, all P <.05)
  • Macular fovea (WMD, ‐5.62 μm [95% CI: ‐7.37, ‐3.87], P =.000)
  • All outer sectors thickness at macula (WMD range, ‐4.68 ~ ‐4.10 μm, all P <.05)
  • Macular volume (WMD, ‐0.21 mm3 [95% CI: ‐0.36, ‐0.06], P <.05)
  • mGCC thickness (WMD, ‐4.18 μm [95% CI: ‐6.07, ‐2.29], P <.05)

The investigators acknowledge limitations of the study, including that most of the studies they analyzed were case-control studies. They also note that, whether individualized diagnosis via OCT is possible was not confirmed by this study, since there aren’t enough diagnostic studies composed of false-, true-positive, or false-, true-negative fractions.

Reference
Zhou WC, Tao JX, Li J. Optical coherence tomography measurements as potential imaging biomarkers for Parkinson’s disease: a systematic review and meta-analysis. Eur J Neurol. Published online October 26, 2020. doi: 10.1111/ene.14613.