DNA methyltransferase expression (DNMT1, DNMT3a and DNMT3b) as a potential biomarker for anti-VEGF diabetic macular edema response

PURPOSE: DNA methylation is involved in Diabetic Retinopathy progression showing a metabolic memory mechanism. However, the association of DNA methyltransferase with diabetic macular edema is still unknown. We aimed to describe the differences in DNA methyltransferase gene expression in patients with different diabetic macular edema responses. METHODS: A total of 27 diabetic patients, aged 59–90 years, were prospectively enrolled in this cross-sectional study. The participants were classified into control group (CG, n = 11), diabetic macular edema responders (rDME, n = 9) and non-responder diabetic macular edema (nrDME, n = 7) after anti-vascular endothelial growth factor (anti-VEGF) treatment. Only cases with a complete ophthalmological examination, digital 133° color fundus, and SD-OCT assessments were used. After RNA extraction and first-strand cDNA synthesis, quantitative real-time PCR was performed with specific primers on the CFX Connect™ Real-Time PCR Detection System to assess differential transcriptional expression patterns. RESULTS: The DNMT1 gene showed a positive correlation (r = 0.617; p = 0.043) with Best Corrected Visual Acuity (BCVA) in CG, a positive correlation (r = 0.917; p = 0.010) with HbA1c in nrDME and a negative correlation (r = −0.659; p = 0.049) with GCL-IPL thickness in rDME. DNMT3A gene showed a positive correlation (r = −0.890; p = 0.001) with Sub-foveal Choroidal thickness in rDME whereas DNMT3b gene showed a negative correlation (r = −0.815; p = 0.007) with HbA1c and RNFL (r = −0.664; p = 0.026) in CG. CONCLUSIONS: Patients with similar metabolic profile risk factors showed associated DNA methyltransferase transcriptional expression patterns differences fitting with the anti-VEGF diabetic macular edema response. Further studies are needed to clarify if these results (1) reflect disease evolution, (2) translate the therapeutic impact, (3) or can help to predict the therapeutic resistance profile.