Correlations between retinal nerve fiber layer thickness and cognitive progression in Parkinson's disease: A longitudinal study

Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline

PURPOSE

To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment.

METHODS

Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted.

RESULTS

Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements.

CONCLUSIONS AND RELEVANCE

Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.

Structural changes in the retina as a potential biomarker in Parkinson's disease: an approach from optical coherence tomography

Introduction

Parkinson's disease (PD) presents challenges in early diagnosis and follow-up due to the lack of characteristic findings. Recent studies suggest retinal changes in PD are possibly indicative of neurodegeneration. We explored these changes using optical coherence tomography (OCT) to assess retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness.

Methods

Thirty PD and non-PD patients were matched according to demographic characteristics and OCT and clinical evaluations to rule out other neurodegenerative and visual diseases.

Results

We observed a significant thinning of the RNFL in patients diagnosed with PD compared to non-PD patients (p = 0.015). Additionally, this reduction in RNFL thickness was found to correlate with the severity of the disease (p = 0.04).

Conclusion

The OCT serves as a tool for quantifying neurodegeneration in PD, showing a significant correlation with disease severity. These findings suggest that OCT could play a crucial role as a potential biomarker in the diagnosis and monitoring of PD.

Association of retinal neurodegeneration with the progression of cognitive decline in Parkinson's disease

Retinal thickness may serve as a biomarker in Parkinson's disease (PD). In this prospective longitudinal study, we aimed to determine if PD patients present accelerated thinning rate in the parafoveal ganglion cell-inner plexiform layer (pfGCIPL) and peripapillary retinal nerve fiber layer (pRNFL) compared to controls. Additionally, we evaluated the relationship between retinal neurodegeneration and clinical progression in PD. A cohort of 156 PD patients and 72 controls underwent retinal optical coherence tomography, visual, and cognitive assessments between February 2015 and December 2021 in two Spanish tertiary hospitals. The pfGCIPL thinning rate was twice as high in PD (β [SE] = -0.58 [0.06]) than in controls (β [SE] = -0.29 [0.06], p < 0.001). In PD, the progression pattern of pfGCIPL atrophy depended on baseline thickness, with slower thinning rates observed in PD patients with pfGCIPL below 89.8 µm. This result was validated with an external dataset from Moorfields Eye Hospital NHS Foundation Trust (AlzEye study). Slow pfGCIPL progressors, characterized by older at baseline, longer disease duration, and worse cognitive and disease stage scores, showed a threefold increase in the rate of cognitive decline (β [SE] = -0.45 [0.19] points/year, p = 0.021) compared to faster progressors. Furthermore, temporal sector pRNFL thinning was accelerated in PD (βtime x group [SE] = -0.67 [0.26] μm/year, p = 0.009), demonstrating a close association with cognitive score changes (β [SE] = 0.11 [0.05], p = 0.052). This study suggests that a slower pattern of pfGCIPL tissue loss in PD is linked to more rapid cognitive decline, whereas changes in temporal pRNFL could track cognitive deterioration.

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease

BACKGROUND

Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence.

METHODS

We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods.

RESULTS

Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12).

CONCLUSIONS

In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials.

Retinal inner nuclear layer thickness in the diagnosis of cognitive impairment explored using a C57BL/6J mouse model

Major neurocognitive disorder (NCD) affects over 55 million people worldwide and is characterized by cognitive impairment (CI). This study aimed to develop a non-invasive diagnostic test for CI based upon retinal thickness measurements explored in a mouse model. Discrimination indices and retinal layer thickness of healthy C57BL/6J mice were quantified through a novel object recognition test (NORT) and ocular coherence tomography (OCT), respectively. Based on criteria from the Diagnostic and statistical manual of mental disorders 5th ed. (DSM-V), a diagnostic test was generated by transforming data into rolling monthly averages and categorizing mice into those with and without CI and those with a high or low decline in retinal layer thickness. Only inner nuclear layer thickness had a statistically significant relationship with discrimination indices. Furthermore, our diagnostic test was 85.71% sensitive and 100% specific for diagnosing CI, with a positive predictive value of 100%. These findings have potential clinical implications for the early diagnosis of CI in NCD. However, further investigation in comorbid mice and humans is warranted.

Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence

Background/Objective

This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT).

Method

The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented.

Results

No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82.

Conclusions

This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.

Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease

Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.

Prenatal psychosocial stress and children's sleep problems: Evidence from the ELSPAC-CZ study

Prenatal exposure to maternal stress may increase the risk of developing sleep problems in childhood. This study examined the association between prenatal stressful life events (PSLE) and children's sleep problems, taking into consideration their trajectory over time. Data were obtained from the Czech portion of the European Longitudinal Cohort Study of Pregnancy and Childhood (ELSPAC-CZ; N = 4,371 children). Mothers reported PSLE using an inventory of 42 life events and child sleep problems at five time-points (child age of 1.5, 3, 5, 7, and 11 years). The association was tested by a Poisson latent growth model, controlling for maternal and family demographics, birth characteristics, maternal depression, and alcohol use in pregnancy. The average rate of sleep problems was 2.06 (p < 0.001) at the age of 1.5 years and the rate of sleep problems decreased in a linear fashion over time (estimate = -0.118; p < 0.001). A higher number of PSLE was associated with a higher rate of sleep problems at the age of 1.5 years (incidence rate ratio [IRR] per interquartile range = 1.08, 95% confidence interval [CI] 1.05-1.12, p < 0.001) and with a reduced rate of decrease in sleep problems between the ages of 1.5 and 11 years (p < 0.001). Thus, PSLE were associated with chronicity of sleep problems in addition to their amount during early childhood. Prenatal exposure to stress may predispose individuals to the development of sleep problems in later life.

Parkinsonism and dementia

The aim of the present review is to summarize literature data on dementia in parkinsonian disorders. Cognitive decline and the gradual development of dementia are considered to be key features in the majority of parkinsonian conditions. The burden of dementia in everyday life of parkinsonian patients and their caregivers is vast and can be even more challenging to handle than the motor component of the disease. Common pathogenetic mechanisms involve the aggregation and spreading of abnormal proteins like alpha-synuclein, tau or amyloid in cortical and subcortical regions with subsequent dysregulation of multiple neurotransmitter systems. The degree of cognitive deterioration in these disorders is variable and ranges from mild cognitive impairment to severe cognitive dysfunction. There is also variation in the number and type of affected cognitive domains which can involve either a single domain like executive or visuospatial function or multiple ones. Novel genetic, biological fluid or imaging biomarkers appear promising in facilitating the diagnosis and staging of dementia in parkinsonian conditions. A significant part of current research in Parkinson's disease and other parkinsonian syndromes is targeted towards the cognitive aspects of these disorders. Stabilization or amelioration of cognitive outcomes represents a primary endpoint in many ongoing clinical trials for novel disease modifying treatments in this field. This article is part of the Special Issue "Parkinsonism across the spectrum of movement disorders and beyond" edited by Joseph Jankovic, Daniel D. Truong and Matteo Bologna.

Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson's Disease?

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson's disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.