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Background/aimsTo determine the incidence of any diabetic retinopathy (any-DR), sight-threatening diabetic retinopathy (STDR) and diabetic macular oedema (DMO) and their risk factors in type 1 diabetes mellitus (T1DM) over a screening programme.MethodsNine-year follow-up, prospective population-based study of 366 patients with T1DM and 15 030 with T2DM. Epidemiological risk factors were as follows: current age, age at DM diagnosis, sex, type of DM, duration of DM, arterial hypertension, levels of glycosylated haemoglobin (HbA1c), triglycerides, cholesterol fractions, serum creatinine, estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR).ResultsSum incidence of any-DR was 47.26% with annual incidence 15.16±2.19% in T1DM, and 26.49% with annual incidence 8.13% in T2DM. Sum incidence of STDR was 18.03% with annual incidence 5.77±1.21% in T1DM, and 7.59% with annual incidence 2.64±0.15% in T2DM. Sum incidence of DMO was 8.46% with annual incidence 2.68±038% in patients with T1DM and 6.36% with annual incidence 2.19±0.18% in T2DM. Cox's survival analysis showed that current age and age at diagnosis were risk factors at p<0.001, as high HbA1c levels at p<0.001, LDL cholesterol was significant at p<0.001, eGFR was significant at p<0.001 and UACR at p=0.017.ConclusionsThe incidence of any-DR and STDR was higher in patients with T1DM than those with T2DM. Also, the 47.26% sum incidence of any-DR in patients with T1DM was higher than in a previous study (35.9%), which can be linked to poor metabolic control of DM. Our results suggest that physicians should be encouraged to pay greater attention to treatment protocols for T1DM in patients.

Background/aimsTo determine the changes in the incidence of diabetic retinopathy (DR), diabetic macular oedema (DMO) and their risk factors in a population-based study of patients with diabetes mellitus (DM) referred to our 16 Primary Health Care Areas (HCAs).MethodsProspective population-based study of a total of 15 396 Caucasian patients with DM, who represent 86.53% of the total patients with DM in our HCAs, were studied over an 8-year follow-up period. All patients were screened with a mean follow-up of 3.18±1.11 times for each patient over the 8 years.ResultsThe yearly mean value of any DR was 8.37±2.19% (8.09%–8.99%); of advanced DR yearly mean value of 0.46±0.22% (0.03–0.78); and of DMO a yearly mean value of 2.19±0.18% (2%–2.49%). A clear increase was observed in the last 3 years, any DR increased from 8.09% in 2007 to 8.99% in 2014, and DMO from 2% in 2007 to 2.49% in 2014. These increases were more evident in some age groups. For patients with any DR aged 41–50 and 51–60 and for patients with advanced DR aged 41–50, 51–60 and 61–70, the increase was more marked, related to an increase in HbA1c values or to patients treated with insulin.ConclusionsAn increase in the incidence of DR and DMO was observed, especially in the younger patients aged between 31 and 70 years. This is linked to bad metabolic control of DM. Our results suggest a greater number of ocular complications in the near future, such as neovascular glaucoma, if these current findings are not addressed.

Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem.