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.

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.

Relevant studies published between January 2010 and June 2021 were identified through relevant databases, including the Science Citation Index Expanded (SCIE) database of Web of Science, PubMed, and Embase, in order to assess the effect of health literacy (HL) intervention on patients with diabetes. A total of 21 articles were eligible. The results showed that: (1) this review involved different HL assessment tools, most of which were self-designed scales and assessment tools focused on measuring functional HL. (2) The differences in glycosylated hemoglobin (HbA1c) (weighted mean difference [WMD] = −0.78, 95% confidence interval [CI]: −0.94, −0.62) and medication adherence (standardized mean difference [SMD] = 1.85, 95% CI: 0.19, 3.52) between the HL intervention group and the usual care group were statistically significant. There was no significant improvement in systolic blood pressure (SMD = −0.05, 95% CI: −0.34, 0.25). Furthermore, this review reported that self-efficacy (SMD = 0.85, 95% CI: 0.65, 1.04) was increased, and the level of HL was improved. In the assessments of risk of bias, 90% of the studies were classified as medium. The quality of the evidence of medication adherence was very low, and the reliability of the conclusions was not enough to confirm the effect of HL.

To determine the association between retinal vasculature changes and stroke. MEDLINE and EMBASE were searched for relevant human studies to September 2015 that investigated the association between retinal vasculature changes and the prevalence or incidence of stroke; the studies were independently examined for their qualities. Data on clinical characteristics and calculated summary odds ratios (ORs) were extracted for associations between retinal microvascular abnormalities and stroke, including stroke subtypes where possible, and adjusted for key variables. Nine cases were included in the study comprising 20 659 patients, 1178 of whom were stroke patients. The retinal microvascular morphological markers used were hemorrhage, microaneurysm, vessel caliber, arteriovenous nicking, and fractal dimension. OR of retinal arteriole narrowing and retinal arteriovenous nicking and stroke was 1.42 and 1.91, respectively, indicating that a small-caliber retinal arteriole and retinal arteriovenous nicking were associated with stroke. OR of retinal hemorrhage and retinal microaneurysm and stroke was 3.21 and 3.83, respectively, indicating that retinal microvascular lesions were highly associated with stroke. Results also showed that retinal fractal dimension reduction was associated with stroke (OR: 2.28 for arteriole network, OR: 1.80 for venular network). Retinal vasculature changes have a specific relationship to stroke, which is promising evidence for the prediction of stroke using computerized retinal vessel analysis.

Previous studies have explored the association between retinal vascular changes and cognitive impairment. The retinal vasculature shares some characteristics with the cerebral vasculature, and quantitative changes in it could indicate cognitive impairment. Hence, a comprehensive meta-analysis was performed to clarify the potential relationship between retinal vascular geometric changes and cognitive impairment. Relevant databases were scrupulously and systematically searched for retinal vascular geometric changes including caliber, tortuosity, and fractal dimension (FD), and for cognitive impairment. The Newcastle-Ottawa Scale was used to evaluate the methodological quality of included studies. RevMan was used to perform the meta-analysis and detect publication bias. Sensitivity analyses were also performed. Five studies that involved 2,343 subjects were finally included in the meta-analysis. The results showed that there was no significant association between central retinal artery equivalents (Z=1.17) or central retinal venular equivalents (Z=1.74) and cognitive impairment (both p>0.05). Similarly, no significant difference was detected in retinal arteriolar tortuosity (Z=0.91) and venular tortuosity (Z=1.31) (both p>0.05). However, the retinal arteriolar FD (mean difference: -0.03, 95% CI: -0.05, -0.01) and venular FD (mean difference: -0.03, 95% CI: -0.05, -0.02) were associated with cognitive impairment. A smaller retinal microvascular FD might be associated with cognitive impairment. Further large-sample and well-controlled original studies are required to confirm the present findings.

AIM: To verify the association between retinopathy, nephropathy, and periodontitis in type 2 diabetic (T2D) patients. METHODS: Several electronic databases were available for our comprehensive search including China National Knowledge Infrastructure (CNKI), Chinese VIP Information (VIP), Wanfang, Web of Science, ScienceDirect and PubMed and were queried for relevant citations (updated to Mar. 2019). RevMan was utilized to perform Meta analysis and publication bias detection. After evaluation of the methodological quality of included studies, a fixed or random effect model was utilized to analyze data from included studies. RESULTS: A total of eight articles were finally included in this Meta analysis. In all 3987 subjects, there were 1207 T2D patients accompanying with microvascular complications and 1734 patients with periodontitis as well. The Meta forest plot presented little heterogeneity of the eight studies (P<0.00001, I2=89%). The total effect demonstrated periodontitis was associated with overall microvascular complications (OR: 1.96, 95%CI: 1.67-2.30, Z=8.25, P<0.00001). Subgroup investigations among the studies in Asian (OR: 2.33, 95%CI: 1.91-2.85) and North American (OR: 1.42, 95%CI: 1.08-1.86) populations confirmed the existed association between retinopathy, nephropathy, and periodontitis. While the strength of such associations between periodontitis and diabetic microvascular complications were more obvious in the Asians than North Americans. All the results indicated that periodontitis was associated with diabetic retinopathy (OR: 3.77, 95%CI: 2.71-5.24), diabetic nephropathy (OR: 1.55, 95%CI: 1.24-1.94) in T2D patients. CONCLUSION: The periodontitis is associated with diabetic retinopathy, diabetic nephropathy among T2D patients and further large sample size clinical trials are in need to confirm the findings.