Retinal asymmetry in multiple sclerosis

Abstract

The diagnosis of multiple sclerosis is based on a combination of clinical and paraclinical tests. The potential contribution of retinal optical coherence tomography (OCT) has been recognized. We tested the feasibility of OCT measures of retinal asymmetry as a diagnostic test for multiple sclerosis at the community level. In this community-based study of 72 120 subjects, we examined the diagnostic potential of the inter-eye difference of inner retinal OCT data for multiple sclerosis using the UK Biobank data collected at 22 sites between 2007 and 2010. OCT reporting and quality control guidelines were followed. The inter-eye percentage difference (IEPD) and inter-eye absolute difference (IEAD) were calculated for the macular retinal nerve fibre layer (RNFL), ganglion cell inner plexiform layer (GCIPL) complex and ganglion cell complex. Area under the receiver operating characteristic curve (AUROC) comparisons were followed by univariate and multivariable comparisons accounting for a large range of diseases and co-morbidities. Cut-off levels were optimized by ROC and the Youden index. The prevalence of multiple sclerosis was 0.0023 [95% confidence interval (CI) 0.00229-0.00231]. Overall the discriminatory power of diagnosing multiple sclerosis with the IEPD AUROC curve (0.71, 95% CI 0.67-0.76) and IEAD (0.71, 95% CI 0.67-0.75) for the macular GCIPL complex were significantly higher if compared to the macular ganglion cell complex IEPD AUROC curve (0.64, 95% CI 0.59-0.69, P = 0.0017); IEAD AUROC curve (0.63, 95% CI 0.58-0.68, P < 0.0001) and macular RNFL IEPD AUROC curve (0.59, 95% CI 0.54-0.63, P < 0.0001); IEAD AUROC curve (0.55, 95% CI 0.50-0.59, P < 0.0001). Screening sensitivity levels for the macular GCIPL complex IEPD (4% cut-off) were 51.7% and for the IEAD (4 μm cut-off) 43.5%. Specificity levels were 82.8% and 86.8%, respectively. The number of co-morbidities was important. There was a stepwise decrease of the AUROC curve from 0.72 in control subjects to 0.66 in more than nine co-morbidities or presence of neuromyelitis optica spectrum disease. In the multivariable analyses greater age, diabetes mellitus, other eye disease and a non-white ethnic background were relevant confounders. For most interactions, the effect sizes were large (partial ω2 > 0.14) with narrow confidence intervals. In conclusion, the OCT macular GCIPL complex IEPD and IEAD may be considered as supportive measurements for multiple sclerosis diagnostic criteria in a young patient without relevant co-morbidity. The metric does not allow separation of multiple sclerosis from neuromyelitis optica. Retinal OCT imaging is accurate, rapid, non-invasive, widely available and may therefore help to reduce need for invasive and more costly procedures. To be viable, higher sensitivity and specificity levels are needed.

Keywords: biomarkers; demyelination; imaging; multiple sclerosis; optic neuritis.

Figure 1

Study flow chart for the UK Biobank participants. Inclusion/exclusion criteria for the macular SD-OCT data used in our analyses are summarized. QC = quality control; SD = spectral domain.

Figure 2

The usefulness of the mGCIPL IEPD and IEAD as a paraclinical test for multiple sclerosis. The graph shows group comparisons. The reference group are patients suffering from multiple sclerosis. The other groups are composed of participants with a range of other diseases. Because patients can have more than one disease, they could be included in more than one group in this analysis. We analysed both the number of diseases co-existing in patients and the type of disease. All analyses were based on statistical comparisons of the AUC between different ROCs (see also Supplementary Fig. 1). (A) The impact of the number of co-morbidities is illustrated. The more diseases co-exist in patients, the less useful the IEPD and IEAD become as a diagnostic test for multiple sclerosis. For all comparisons, the IEPD (black bars) performs better than the IEAD (grey bars). (B) The influence of other disease groups (ICD-10) on making the diagnosis of multiple sclerosis. The best results are achieved for the conditions listed on the top of the graph (control subjects). The test is clinically useful if located to the right of the vertical reference line (>0.7). This is the case for horizontal bars where the small vertical tick (ROC AUC value, indicated by arrow) on top of the horizontal bar (95% CI) is located to the right of the vertical reference line. Supplementary Fig. 1 illustrates this step in more detail. The patient numbers per group for the comparison to patients with multiple sclerosis (n = 144) are presented to the right of the bar chart and respective demographic data are summarized in Supplementary Table 1.

Figure 3

Univariable and multivariable analysis of the IEPD and IEAD as a supportive test for multiple sclerosis. The graph shows the group comparison between people suffering from multiple sclerosis and the control group from Table 3. (A) In the univariate analysis the IEPD provides a robust supportive diagnostic test for multiple sclerosis (OR 1.11) with narrow 95% CI (1.09–1.13). The multivariable analyses show that significance is retained for all but three combinations, highlighted in red (IEPD at age >65 years, IEPD in non-white subjects, IEPD in patients with diabetes mellitus). (B) The IEAD has very similar properties in the univariate and multivariable analyses with higher age, non-white ethnicity and diabetes mellitus being relevant covariates.