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Purpose: To analyze and compare metabolic, lifestyle and mental health parameters in relatives and people-with-T2DM (PDM) with and without support.Patients and Methods: We included 160 patients with < 5 years of diagnosis of T2DM, without disabling complications, and non-smokers, attending a multidisciplinary program for diabetes control, and their accompanying relatives. If the patients or relatives abandoned the program, we contacted them and asked to take laboratory tests and answer surveys regarding anxiety, depression, and perception of their family support. Variables distribution was assessed with the Kolmogorov–Smirnov test. We used ANOVA or Kruskal Wallis Tests, according to variable distribution. Frequencies and percentages are used for categorical values and analyzed with a chi-square test. We separated the participants in four groups: relatives with and without support and PDM with and without support.Results: We included 160 participants, age 51± 10, and 54.3% women. Total cholesterol (188± 36 vs 204± 43 vs 170± 34 vs 181± 35 mg/dL, p=0.001), LDL-cholesterol (113± 35 vs 125± 27 vs 101 ± 30 vs 109± 29, p=0.008), and non-HDL cholesterol (143± 32 vs 154 ± 30 vs 129± 33 vs 135± 35 mg/dL, p=0.010) were higher in the group without support. Although patients without family support had lower values, they did not achieve metabolic goals. Weight (75± 17 vs 77± 19 vs 74.2± 10.5 vs 90.2± 17.3 kg) and body mass index (28.9± 4.8 vs 30.1± 4.7 vs 27.4± 3.3 vs 33± 4.3 kg/m2) were higher in PDM without family support (p< 0.001 for both).Conclusion: Support in PDM and their families is important in metabolic control. However, raising awareness among family members to screen for diabetes and changes in lifestyle are points to improve. Including the evaluation of social and family support will allow a more complete assessment to identify barriers to achieving goals. [ABSTRACT FROM AUTHOR]

Introduction: Anxiety disorders (AXD) are among the most prevalent mental health conditions in patients with type 2 diabetes (T2D). Previous data have established an association of other psychiatric conditions with poor metabolic control and increased odds of diabetes-related complications. Nonetheless, follow-up information about the effects of AXD on the metabolic control of patients with TD2 is still limited. Objective: Evaluate the effects of AXD on the metabolic parameters of patients with T2D over 12 months of follow-up in a multidisciplinary comprehensive care model. Methods: Prospective study of T2D subjects enrolled in a comprehensive care program with follow-up at 3 and 12 months of treatment. Patients were assessed using the Mini-International Neuropsychiatric Interview and the Hospital Anxiety and Depression Scale (HADS). We registered clinical and metabolic characteristics from each visit. Metabolic parameters over time were analyzed with a mixed model of repeated measures using AXD and time as interaction variables. Results: Our sample included 2703 patients at baseline, and 1161 (43%) subjects continued the follow-up at 12 months. The AXD group had more females, lower age, and fewer years of formal education compared with subjects without AXD at baseline, 3 and 12 months. Patients with AXD also reported higher mean fasting glucose at three months, and higher HbA1c at three and 12 months. Our MMRM for HbA1c reported significant differences over time in subjects with and without AXD. The differences in means between groups increased from 0.17% at three months to 0.31% at 12 months. The variables from the HADS anxiety score, sex, age, years of diagnosis, and insulin treatment were also associated with HbA1c parameters over time. Conclusion: Patients with AXD had the worst glycemic control at 3 and 12 months of follow-up. HbA1c differences in patients with AXD compared with non-AXD subjects increases over time in association with anxiety symptoms. [ABSTRACT FROM AUTHOR]

Diabetic retinopathies are important disabling conditions. Micro-RNAs (miRNAs) are regulators of gene expression and diseases can change their expression. Our aim was to analyze the expression of miRNAs in serum and vitreous samples from patients with diabetic retinopathies. The following groups and number of individuals were included: proliferative diabetic retinopathy (PDR) (n = 16), diabetic macular edema (DME) (n = 17), and idiopathic epiretinal membrane (IEM) as non-diabetic controls (n = 23). The initial miRNA expression was explored using TaqMan low-density arrays (TLDAs) with subsequent validation through a quantitative polymerase chain reaction (qPCR). Target genes were identified through bioinformatic tools for enrichment analysis. The TLDAs revealed the following miRNAs with differential expression in terms of PDR vs. IEM: miR-320a-3p, miR-92a-3p, and miR-375-3p in the serum, with miR-541-5p and miR-223-5p in the vitreous samples. DME vs IEM: miR-486-5p, miR-145-5p, miR-197-3p, and miR-125b-5p in the serum, and miR-212-3p in vitreous samples. PDR vs. DME: miR-486-5p, miR-100-5p, miR-328-3p, miR-660-5p, and miR-145 in the serum and none in the vitreous samples. Validation was confirmed only for miR-145, miR-92a, and miR-375 in the serum. The relevant enriched pathways for these three validated miRNAs, miR-145, miR-92a, and miR-375 were the vascular endothelial growth factor and its receptor, hepatocyte growth factor receptor, epidermal growth factor, focal adhesion, and phosphoinositide 3-kinase. Our results support the involvement of miRNAs in the pathophysiology of diabetic retinopathies and reinforce their potential as biomarkers or therapeutic resources. [ABSTRACT FROM AUTHOR]