Wentao Han

Spermidine, a natural polyamine, exists in almost all human tissues, exhibiting broad properties like anti-aging, autophagy induction, anti-inflammation, anti-oxidation, cell proliferation activation, and ion channel regulation. Considering that spermidine is already present in human nutrition, recent studies targeting supplementing exogenous sources of this polyamine appear feasible. The protective role of spermidine in various systems has been illuminated in the literature, while recent progress of spermidine administration in ocular diseases remains to be clarified. This study shows the current landscape of studies on spermidine and its potential to become a promising therapeutic agent to treat ocular diseases: glaucoma, optic nerve injury, age-related macular degeneration (AMD), cataracts, dry eye syndrome, and bacterial keratitis. It also has the potential to become a potent biomarker to predict keratoconus (KC), cataracts, uveitis, glaucoma, proliferative diabetic retinopathy (PDR), proliferative vitreoretinopathy (PVR), and retinopathy of prematurity (ROP). We also summarize the routes of administration and the effects of spermidine at different doses.

Background: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. Objective: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. Methods: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Results: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. Conclusions: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

BACKGROUND Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. OBJECTIVE The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. METHODS Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. RESULTS A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. CONCLUSIONS Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Aims The aim of this study was to investigate the association between retinol-binding protein 4 (RBP4) and diabetic retinopathy (DR) among patients with type 2 diabetes mellitus (T2DM). Methods Databases PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, VIP, and Wangfang were searched to July 30, 2019. The Newcastle–Ottawa Scale was applied to assess the quality of all identified studies, and those qualified were included in the meta-analysis. The Chi squared Q test and I² statistics were conducted to evaluate heterogeneity. Standardized mean differences (SMD) and 95% confidence intervals (CI) among RBP4 within the DR and T2DM without retinopathy (DWR) groups were pooled using the random effects model depending on the heterogeneity. Subgroup analyses were conducted among the groups having different diabetes duration, detection methods, body mass index, and total cholesterol and triglyceride levels. The funnel plot was used to assess publication bias. Results Nineteen observational studies were included in our meta-analysis. RBP4 was significantly higher in both nonproliferative DR (SMD: 0.72, 95% CI 0.48–0.95, P < 0.00001) and proliferative DR (SMD: 2.68, 95% CI 1.69–3.67, P < 0.00001) groups despite high heterogeneity (I² = 87 and 97% in DR and PDR groups, respectively). Significant differences were noted among most subgroups (P < 0.05). Among those accompanied by hypercholesterolemia, the association between RBP4 and DR were unclear (P = 0.09). Conclusions Elevated RBP4 is strongly associated with DR and may play an essential role in its progression. Additional large-scale controlled studies are needed to confirm these findings.

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.