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Background Diabetic retinopathy (DR) is the primary oculopathy causing blindness in diabetic patients. Currently, there is increasing interest in the role of lipids in the development of diabetic retinopathy, but it remains controversial. Remnant cholesterol (RC) is an inexpensive and easily measurable lipid parameter; however, the relationship between RC and DR in type 2 diabetes mellitus (T2DM) has not been elucidated. This research investigates the relevance between RC levels and DR severity while building a risk prediction model about DR. Methods In this single-centre retrospective cross-sectional study. Each hospitalised T2DM patient had no oral lipid-lowering drugs in the past three months, and coronary angiography showed epicardial coronary artery stenosis of less than 50% and completed seven-field stereo photographs, fluorescein fundus angiography, and optical coherence tomography detection. The RC value is calculated according to the internationally recognised formula. Binary logistic regression was used to correct confounding factors, and the receiver operating characteristic (ROC) analysis was used to identify risk factors and assess the nomogram’s diagnostic efficiency. Results A total of 456 T2DM patients were included in the study. The RC levels in the DR team was higher [0.74 (0.60–1.12) mmo/l vs 0.54 (0.31–0.83) mmol/l P < 0.001] in the non-DR team. After adjusting for confounding elements, RC levels are still associated with DR risk (OR = 5.623 95%CI: 2.996–10.556 P < 0.001). The ratio of DR in every stage (except mild non-proliferative diabetic retinopathy) and DME in the high RC level team were further increased compared to the low-level team (all P < 0.001). After ROC analysis, the overall risk of DR was predicted by a nomogram constructed for RC, diabetes duration, and the neutrophil-lymphocyte ratio as 0.758 (95%CI 0.714–0.802 P < 0.001). Conclusions High RC levels may be a potential risk factor for diabetic retinopathy, and the nomogram does better predict DR. Despite these essential findings, the limitation of this study is that it is single-centred and small sample size analysis.

Background The triglyceride glucose (TyG) index reflects insulin resistance; the latter being associated with mild cognitive impairment (MCI). Objective To investigate the clinical value of the TyG index to identify MCI in patients living with type 2 diabetes (T2D) using a cross-sectional study. Methods This cross-sectional study was performed on 517 patients with T2D. The diagnosis of MCI was based on criteria established by the National Institute on Aging-Alzheimer’s Association workgroup, and patients were divided into the MCI group and the normal cognitive function (NCF) group. The logistic regression analysis determines whether the TyG index is related to MCI. Subsequently, we constructed the receiver operating characteristic curve (ROC) and calculated the area under the curve (AUC). The nomogram model of the influence factor was established and verified. Results Compared to the type 2 diabetes-normal cognitive function (T2D-NCF) group, the MCI subjects were olderand had higher TyG indexes, lower cognitive scores, and lower education levels (p < 0.01). After adjusting for the confounders, the TyG index was associated with MCI (OR = 7.37, 95% CI = 4.72–11.50, p < 0.01), and TyG-BMI was also associated with MCI (OR = 1.02, 95% CI = 1.01–1.02, p<0.01). The TyG index AUC was 0.79 (95% CI = 0.76–0.83). The consistency index of the nomogram was 0. 83[95% CI (0. 79, 0. 86)]. Conclusion Our results indicate that the TyG index and TyG-BMI are associated with MCI in T2D patients, and the TyG index is an excellent indicator of the risk of MCI in T2D patients. The nomogram incorporating the TyG index is useful to predict MCI risk in patients with T2D.

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ENCD (http://www.bio-server.cn/ENCD/) is a manually curated database that provides comprehensive experimentally supported associations among endocrine system diseases (ESDs) and long non-coding ribonucleic acid (lncRNAs). The incidence of ESDs has increased in recent years, often accompanying other chronic diseases, and can lead to disability. A growing body of research suggests that lncRNA plays an important role in the progression and metastasis of ESDs. However, there are no resources focused on collecting and integrating the latest and experimentally supported lncRNA–ESD associations. Hence, we developed an ENCD database that consists of 1379 associations between 35 ESDs and 501 lncRNAs in 12 human tissues curated from literature. By using ENCD, users can explore the genetic data for diseases corresponding to the body parts of interest as well as study the lncRNA regulating mechanism for ESDs. ENCD also provides a flexible tool to visualize a disease- or gene-centric regulatory network. In addition, ENCD offers a submission page for researchers to submit their newly discovered endocrine disorders-genetic data entries online. Collectively, ENCD will provide comprehensive insights for investigating the ESDs associated with lncRNAs. Database URL http://www.bio-server.cn/ENCD