Developing retinal biomarkers of neurological disease: an analytical perspective

The inaccessibility of the brain poses a problem for neuroscience. Scientists have traditionally responded by developing biomarkers for brain physiology and disease. The retina is an attractive source of biomarkers since it shares many features with the brain. Some even describe the retina as a ‘window’ to the brain, implying that retinal signs are analogous to brain disease features. However, new analytical methods are needed to show whether or not retinal signs really are equivalent to brain abnormalities, since this requires greater evidence than direct associations between retina and brain. We, therefore propose a new way to think about, and test, how clearly one might see the brain through the retinal window, using cerebral malaria as a case study

Fiat Lux: the effect of illuminance on acuity testing

PURPOSE: To determine the effect of changing illuminance on visual and stereo acuity. METHODS: Twenty-eight subjects aged 21 to 60 years were assessed. Monocular visual acuity (ETDRS) of emmetropic subjects was assessed under 15 different illuminance levels (50–8000 lux), provided by a computer controlled halogen lighting rig. Three levels of myopia (−0.50DS, −1.00DS & 1.50DS) were induced in each subject using lenses and visual acuity (VA) was retested under the same illuminance conditions. Stereoacuity (TNO) was assessed under the same levels of illuminance. RESULTS: A one log unit change in illuminance level (lx) results in a significant change of 0.060 LogMAR (p < 0.001), an effect that is exacerbated in the presence of induced myopic refractive error (p < 0.001). Stereoacuity scores demonstrate statistically significant overall differences between illuminance levels (p < 0.001). CONCLUSIONS: The findings of this study demonstrate that changes in illuminance have a statistically significant effect on VA that may contribute to test/retest variability. Increases in illuminance from 50 to 500 lx resulted in an improved VA score of 0.12 LogMAR. Differences like these have significant clinical implications, such as false negatives during vision screening and non-detection of VA deterioration, as the full magnitude of any change may be hidden. In research where VA is a primary outcome measure, differences of 0.12 LogMAR or even less could affect the statistical significance and conclusions of a study. It is recommended that VA assessment always be performed between 400 lx and 600 lx, as this limits any effect of illuminance change to 0.012 LogMAR

Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

There is an emerging need in clinical research to accurately predict patients disease status and disease progression by optimally integrating multivariate clinical information. Clinical data is often collected over time for multiple biomarkers of different types (e.g. continuous, binary, counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a 5-year follow up period

Oral bisphosphonates and risk of cancer of oesophagus, stomach, and colorectum: case-control analysis within a UK primary care cohort

Objective To examine the hypothesis that risk of oesophageal, but not of gastric or colorectal, cancer is increased in users of oral bisphosphonates

Spatial Linear Mixed Effects Modelling for OCT Images: SLME Model.

Much recent research focuses on how to make disease detection more accurate as well as "slimmer", i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated. We propose a spatial explanatory modelling approach that combines Optical Coherence Tomography (OCT) retinal imaging data with clinical information. Our model consists of a spatial linear mixed effects inference framework, which innovatively models the spatial topography of key information via mixed effects and spatial error structures, thus effectively modelling the shape of the thickness map. We show that our spatial linear mixed effects (SLME) model outperforms traditional analysis-of-variance approaches in the analysis of Heidelberg OCT retinal thickness data from a prospective observational study, involving 300 participants with diabetes and 50 age-matched controls. Our SLME model has a higher power for detecting the difference between disease groups, and it shows where the shape of retinal thickness profiles differs between the eyes of participants with diabetes and the eyes of healthy controls. In simulated data, the SLME model demonstrates how incorporating spatial correlations can increase the accuracy of the statistical inferences. This model is crucial in the understanding of the progression of retinal thickness changes in diabetic maculopathy to aid clinicians for early planning of effective treatment. It can be extended to disease monitoring and prognosis in other diseases and with other imaging technologies

Visual risk factors for falls in older adults: a case-control study

BACKGROUND: Falls are the second leading  cause of accidental deaths worldwide mainly in older people. Older people have poor vision and published evidence suggests that it is a risk factor for falls. Less than half of falls clinics assess vision as part of the multi-factorial assessment of older adults at risk of falls despite vision being an essential input for postural stability. The aim of our study was to investigate the relationship between all clinically assessed visual functions and falls amongst older adults in a prospective observational individually age-matched case control study. METHODS: Visual acuity (VA), contrast sensitivity (CS), depth perception, binocular vision and binocular visual field were measured using routinely used clinical methods in falls participants (N = 83) and non-falls participants (N = 83). Data were also collected on socio-demographic factors, general health, number of medications, health quality, fear of falling and physical activity. Logistic regression analysis was carried out to determine key visual and non-visual risk factors for falls whilst adjusting for confounding covariates. RESULTS: Older adults have an increased risk of experiencing a fall if they have reduced visual function (odds ratio (OR): 3.49, 1.64-7.45, p = 0.001), specifically impaired stereoacuity worse than 85” of arc (OR: 3.4, 1.20-9.69, p = 0.02) and reduced (by 0.15 log unit) high spatial frequency CS (18 cpd) (OR:1.40, 1.12-1.80, p = 0.003). Older adults with a hearing impairment are also at higher risk of falls (OR: 3.18, 95% CI: 1.36-7.40, p = 0.007). The risk decreases with living in a less deprived area (OR: 0.74, 0.64-0.86, <0.001), or socialising more out of the home (OR: 0.75, 0.60-0.93, p = 0.01). CONCLUSIONS: The combination of social, behavioural and biological determinants are significant predictors of a fall. The non-visual risk factors include older adults, living in deprived neighbourhoods, socialising less outside of the home and those who have a hearing impairment. Impaired functional visual measures; depth perception and contrast are significant visual risk factors for falls above visual acuity