Identifying mechanisms of diseases with complex inheritance patterns, such as macular telangiectasia type 2, is challenging. A link between macular telangiectasia type 2 and altered serine metabolism has been established previously. METHODS Through exome sequence analysis of a patient with macular telangiectasia type 2 and his family members, we identified a variant in SPTLC1 encoding a subunit of serine palmitoyltransferase (SPT). Because mutations affecting SPT are known to cause hereditary sensory and autonomic neuropathy type 1 (HSAN1), we examined 10 additional persons with HSAN1 for ophthalmologic disease. We assayed serum amino acid and sphingoid base levels, including levels of deoxysphingolipids, in patients who had macular telangiectasia type 2 but did not have HSAN1 or pathogenic variants affecting SPT. We characterized mice with low serine levels and tested the effects of deoxysphingolipids on human retinal organoids. RESULTS Two variants known to cause HSAN1 were identified as causal for macular telangiectasia type 2: of 11 patients with HSAN1, 9 also had macular telangiectasia type 2. Circulating deoxysphingolipid levels were 84.2% higher among 125 patients with macular telangiectasia type 2 who did not have pathogenic variants affecting SPT than among 94 unaffected controls. Deoxysphingolipid levels were negatively correlated with serine levels, which were 20.6% lower than among controls. Reduction of serine levels in mice led to increases in levels of retinal deoxysphingolipids and compromised visual function. Deoxysphingolipids caused photoreceptor-cell death in retinal organoids, but not in the presence of regulators of lipid metabolism. CONCLUSIONS Elevated levels of atypical deoxysphingolipids, caused by variant SPTLC1 or SPTLC2 or by low serine levels, were risk factors for macular telangiectasia type 2, as well as for peripheral neuropathy.

Topic Systematic review of risk factors for non-adherence and non-persistence to intravitreal anti-vascular endothelial growth factor (anti-VEGF) injection therapy for neovascular age-related macular degeneration (nAMD). Clinical Relevance Lack of adherence (non-adherence) or under-treatment (non-persistence) with respect to evidence from clinical trials remains a significant barrier to optimizing real-world outcomes for patients with nAMD. Contributing factors and strategies to address this are poorly understood. Methods Studies that reported factors for non-adherence and/or non-persistence to anti-VEGF therapy as well as studies examining strategies to improve this were included. Trial eligibility and data extraction were conducted according to Cochrane review methods. Risk of bias was assessed using the Mixed Method Assessment Tool and certainty of evidence evaluated according to the GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative Research) tool. Data were collated descriptively. Results Of the 1284 abstract results screened, 124 articles were assessed in full and 37 studies met the inclusion criteria. Definitions of non-adherence and non-persistence varied or were not reported. Non-persistence occurred early with up to 50% of patients stopping treatment by 24 months. High rates of non-adherence were similarly reported, occurring in 32 – 95% of patients. Certainty of this finding was downgraded to moderate level due to heterogeneity in definitions used across studies. Multiple factors determine non-adherence and non-persistence, including at condition, therapy, patient, social/economic and health systems/health-care team level. Moderate quality evidence points to lower baseline vision and poorer response to treatment as condition-related variables. The effects of other factors were of lower certainty, predominantly due to small numbers and potential biases in retrospective assessment. Although many factors are non-modifiable (e.g., patient co-morbidity), other factors are potentially correctable (e.g., lack of transport or mismatched patient expectations). Evidence on strategies to improve adherence and persistence is limited, but where available, these have proven effective. Conclusions Awareness of factors related to poor patient adherence and persistence in nAMD could help identify at-risk populations and improve real world outcomes. Further work is required to develop uniform definitions as well as establishing high quality evidence on interventions that can be easily implemented.

Over a follow-up of 24 months, vision improved in diabetic macular edema eyes after treatment with dexamethasone implants, both in eyes that were treatment naive and eyes refractory to anti-vascular endothelial growth factor treatment; however, improvement was greater in naive eyes.

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PurposeWe aimed to investigate biomarkers and predictive factors for visual and anatomical outcome in patients with naïve diabetic macular edema (DME) who underwent small gauge pars plana vitrectomy (PPV) with internal limiting membrane (ILM) peeling as a first line treatment.DesignMulticenter, retrospective, interventional study.Participants120 eyes from 120 patients with naïve DME treated with PPV and ILM peeling with a follow up of 24 months.MethodsChange in baseline best corrected visual acuity (BCVA) and central subfoveal thickness (CST) 1, 6, 12 and 24 months after surgery. Predictive value of baseline BCVA, CST, optical coherence tomography (OCT) features (presence of subretinal fluid (SRF) and photoreceptor damage), and time between DME diagnosis and surgery. Additional treatment for DME needed. Intra- and post-operative complications (cataract rate formation, increased intraocular pressure).Main outcome measuresThe correlation between baseline characteristics and BCVA response (mean change from baseline; categorized improvement ≥5 or ≥10; Early Treatment Diabetic Retinopathy Study (ETDRS) letters) 12 and 24 months after surgery.ResultsMean BCVA was 0.66 ± 0.14 logMAR, 0.52 ± 0.21 logMAR, and 0.53 ± 0.21 logMAR (p<0.001) at baseline, 12 and 24 months, respectively. Shorter time from DME diagnosis until PPV (OR: 0.98, 95% CI: 0.97–0.99, p<0.001) was a predictor for good functional treatment response (area under the curve 0.828). For every day PPV is postponed, the patient’s chances to gain ≥5 letters at 24 months decrease by 1.8%.Presence of SRF was identified as an anatomical predictor of a better visual outcome, (OR: 6.29, 95% CI: 1.16–34.08, p = 0.033). Safety profile was acceptable.ConclusionsOur results reveal a significant functional and anatomical improvement of DME 24 months after primary PPV, without the need for additional treatment. Early surgical intervention and presence of SRF predict good visual outcome. These biomarkers should be considered when treatment is chosen.

This study provides the first long-term evidence that DEX implant has the potential to not only delay progression of DR and PDR development, but may also improve DR severity over 24 months. Better understanding of the effects of corticosteroids will help guide its use in the treatment pathway of DR.

Purpose To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. Methods A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. Results The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders’ compartmentalization was higher than the mean score for intra-grader group comparison. Conclusion The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.